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Forecasting with the Microsoft Time Series Data Mining Algorithm
 
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Imagine taking historical stock market data and using data science to more accurately predict future stock values. This is precisely the aim of the Microsoft Time Series data mining algorithm.. MSBI - SSAS - Data Mining - Time Series. In this video you will learn the theory of Time Series Forecasting. You will what is univariate time series analysis, AR, MA, ARMA vesves ARIMA modelling and how to use these models to do forecast.. I am sorry for my poor english. I hope it helps you. when i take the data mining course, i had searched it but i couldnt. So i decided to share this video with you.
Views: 859 Fidela Aretha
Apriori Algorithm (Associated Learning) - Fun and Easy Machine Learning
 
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Apriori Algorithm (Associated Learning) - Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Limited Time - Discount Coupon Apriori Algorithm The Apriori algorithm is a classical algorithm in data mining that we can use for these sorts of applications (i.e. recommender engines). So It is used for mining frequent item sets and relevant association rules. It is devised to operate on a database containing a lot of transactions, for instance, items brought by customers in a store. It is very important for effective Market Basket Analysis and it helps the customers in purchasing their items with more ease which increases the sales of the markets. It has also been used in the field of healthcare for the detection of adverse drug reactions. A key concept in Apriori algorithm is that it assumes that: 1. All subsets of a frequent item sets must be frequent 2. Similarly, for any infrequent item set, all its supersets must be infrequent too. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 69404 Augmented Startups
Data Mining Lecture - - Finding frequent item sets | Apriori Algorithm | Solved Example (Eng-Hindi)
 
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In this video Apriori algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining in hindi, Finding frequent item sets, data mining, data mining algorithms in hindi, data mining lecture, data mining tools, data mining tutorial,
Views: 265275 Well Academy
EM algorithm: how it works
 
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Full lecture: http://bit.ly/EM-alg Mixture models are a probabilistically-sound way to do soft clustering. We assume our data is sampled from K different sources (probability distributions). The expectation maximisation (EM) algorithm allows us to discover the parameters of these distributions, and figure out which point comes from each source at the same time.
Views: 200442 Victor Lavrenko
Introduction to Data Mining in SQL Server Analysis Services
 
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Data mining is one of the key hidden gems inside of Analysis Services but has traditionally had a steep learning curve. In this session, you'll learn how to create a data mining model to predict who is the best customer for you and learn how to use other algorithms to spend your marketing model wisely. You'll also see how to use Time Series analysis for budget and forecast prediction. Finally, you'll learn how to integrate data mining into your application through SSIS or custom coding.
Views: 12199 PASStv
Anomaly Detection: Algorithms, Explanations, Applications
 
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Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly "alarms" to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning. See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/
Views: 17690 Microsoft Research
Data Mining  Association Rule - Basic Concepts
 
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short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.
Strategies for scaling up data mining algorithms
 
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In todayΓÇÖs world, data is generated by and collected from a myriad of disciplines such as mechanical systems, sensor network-based Earth science systems, hardware infrastructures, and information networks. Many of the existing data analysis algorithms do not scale to such large data sets. In this talk, I will present some of our work in speeding up existing data mining algorithms to scale to very large data sets. The first technique will describe how outlier detection can be done in an efficient fashion using an indexing strategy and parallel computing on clusters. This will be followed by a discussion on a general framework for checking model fidelity in very large loosely coupled distributed systems and how the framework can be adapted for system health monitoring.
Views: 25 Microsoft Research
Prediction of Student Results #Data Mining
 
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We used WEKA datamining s-w which yields the result in a flash.
Views: 34242 GRIETCSEPROJECTS
kNN Machine Learning Algorithm - Excel
 
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kNN, k Nearest Neighbors Machine Learning Algorithm tutorial. Follow this link for an entire Intro course on Machine Learning using R, did I mention it's FREE: https://www.youtube.com/playlist?list=PLjPbBibKHH18I0mDb_H4uP3egypHIsvMn Also, be sure to check out my channel for over 300 tutorials on Excel, R, Statistics, basic Math, and more.
Views: 71514 Jalayer Academy
The Power of Simple Algorithms: From Data Science to Biological Systems
 
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In this talk I will discuss the power of simple, randomized methods such as hashing, importance sampling, and stochastic iteration in data science and machine learning. In particular, I will overview my efforts to apply these methods to core linear algebraic primitives. I will describe a new class of iterative sampling algorithms, which give state-of-the-art theoretical and empirical performance for regression problems, low-rank matrix approximation, and kernel methods. I will demonstrate that sampling can be surprisingly powerful, giving, for example, sublinear time near-optimal low-rank approximation algorithms for general positive semidefinite matrices. I will conclude by discussing how understanding many of the same simple, randomized algorithms for data applications can be used to study computation in biological systems, including noisy decision making in social insect colonies. See more at https://www.microsoft.com/en-us/research/video/the-power-of-simple-algorithms-from-data-science-to-biological-systems/
Views: 1283 Microsoft Research
Association analysis: Frequent Patterns, Support, Confidence and Association Rules
 
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This lecture provides the introductory concepts of Frequent pattern mining in transnational databases.
Views: 66482 StudyKorner
Data mining algorithms with SQL Server and R part 1   Dejan Sarka HD
 
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Breakout session from DevWeek 2015 http://devweek.com/ DevWeek is the UK’s leading conference for professional software developers, architects and analysts. With insights on the latest technologies, best practice and frameworks from industry-leading experts, plus hands-on workshop sessions, DevWeek is your chance to sharpen your skills - and ensure every member of your team is up to date. Please visit http://devweek.com/ for information on the latest event. ----------------------------------------­----------------------------------------­----- DevWeek is part of DevWeek Events, a series of software development conferences and workshops, including DevWeek's sister conference 'Software Architect' (http://software-architect.co.uk/), brought to you by Publicis UK. ----------------------------------------­----------------------------------------­-----
Views: 581 DevWeek Events
Learn Data Science in 3 Months
 
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I've created a 3 month curriculum to help you go from absolute beginner to proficient in the art of data science! This open source curriculum consists of purely free resources that I’ve compiled from across the Web and has no prerequisites, you don’t even have to have coded before. I’ve designed it for anyone who wants to improve their skills and find paid work ASAP, ether through a full-time position or contract work. You’ll be learning a host of tools like SQL, Python, Hadoop, and even data storytelling, all of which make up the complete data science pipeline. Curriculum for this video: https://github.com/llSourcell/Learn_Data_Science_in_3_Months Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Week 1 - Learn Python - EdX https://www.edx.org/course/introduction-python-data-science-2 - Siraj Raval https://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU Week 2 - Statistics & Probability - KhanAcademy https://www.khanacademy.org/math/statistics-probability Week 3 - Data Pre-processing, Data Vis, Exploratory Data Analysis - EdX https://www.edx.org/course/introduction-to-computing-for-data-analysis Week 4 - Kaggle Project #1 Week 5-6 - Algorithms & Machine Learning - Columbia https://courses.edx.org/courses/course-v1:ColumbiaX+DS102X+2T2018/course/ Week 7 - Deep Learning - Part 1 and 2 of DL Book https://www.deeplearningbook.org/ - Siraj Raval https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3 Week 8 - Kaggle Project #2 Week 9 - Databases (SQL + NoSQL) - Udacity https://www.udacity.com/course/intro-to-relational-databases--ud197 - EdX https://www.edx.org/course/introduction-to-nosql-data-solutions-2 Week 10 - Hadoop & Map Reduce + Spark - Udacity https://www.udacity.com/course/intro-to-hadoop-and-mapreduce--ud617 - Spark Workshop https://stanford.edu/~rezab/sparkclass/slides/itas_workshop.pdf Week 11 - Data Storytelling - Edx https://www.edx.org/course/analytics-storytelling-impact-1 Week 12- Kaggle Project #3 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hiring? Need a Job? See our job board!: www.theschool.ai/jobs/ Need help on a project? See our consulting group: www.theschool.ai/consulting-group/ Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 307730 Siraj Raval
Apriori algorithm example data mining
 
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what is apriori algorithm in data mining? Apriori is an algorithm for frequent item set mining and association rule learning over transactional databases.visit below link for examples http://funtwocode.blogspot.in/2017/08/apriori-algorithm-example.html ******************************************** MORE DATA MINING ALGORITHM PLAYLIST IS ON BELOW LINK: https://www.youtube.com/watch?v=JZepOmvB514&list=PLNmFIlsXKJMmekmO4Gh6ZBZUVZp24ltEr book name : Techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar
Views: 95982 fun 2 code
Market Basket Analysis | Association Rules | R Programming | Data Prediction Algorithm
 
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In this video I've talked about the theory related to market basket analysis. Where I explained about its background and the components like support, confidence and lift. In the next video I'll talk about the code to achieve the association rules by applying market basket analysis in R.
Views: 12097 Data Science Tutorials
Datamining in Science: Mining Patterns in Protein StructuresΓÇöAlgorithms and Applications
 
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With the data explosion occurring in sciences, utilizing tools to help analyze the data efficiently is becoming increasingly important. This session will describe tools included with SQL Server (Yukon), and Wei Wang will describe the MotifSpace projectΓÇöa comprehensive database of candidate spatial protein motifs based on recently developed data mining algorithms. One of the next great frontiers in molecular biology is to understand and predict protein function. Proteins are simple linear chains of polymerized amino acids (residues) whose biological functions are determined by the three-dimensional shapes that they fold into. A popular approach to understanding proteins is to break them down into structural sub-components called motifs. Motifs are recurring structural and spatial units that are frequently correlated with specific protein functions. Traditionally, the discovery of motifs has been a laborious task of scientific exploration. In this talk, I will discuss recent data-mining algorithms that we have developed for automatically identifying potential spatial motifs. Our methods automatically find frequently occurring substructures within graph-based representations of proteins. The complexity of protein structures and corresponding graphs poses significant computational challenges. The kernel of our approach is an efficient subgraph-mining algorithm that detects all (maximal) frequent subgraphs from a graph database with a user-specified minimal frequency.
Views: 119 Microsoft Research
Apriori Algorithm Video, KDD Knowledge Discovery in Database
 
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This is a video demonstration of finding representative rules and sets using the Apriori algorithm.
Views: 32773 Laurel Powell
K Means Clustering Algorithm | K Means Example in Python | Machine Learning Algorithms | Edureka
 
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** Python Training for Data Science: https://www.edureka.co/python ** This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) series presents another video on "K-Means Clustering Algorithm". Within the video you will learn the concepts of K-Means clustering and its implementation using python. Below are the topics covered in today's session: 1. What is Clustering? 2. Types of Clustering 3. What is K-Means Clustering? 4. How does a K-Means Algorithm works? 5. K-Means Clustering Using Python Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm Subscribe to our channel to get video updates. Hit the subscribe button above. How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you: 1. Programmatically download and analyze data 2. Learn techniques to deal with different types of data – ordinal, categorical, encoding 3. Learn data visualization 4. Using I python notebooks, master the art of presenting step by step data analysis 5. Gain insight into the 'Roles' played by a Machine Learning Engineer 6. Describe Machine Learning 7. Work with real-time data 8. Learn tools and techniques for predictive modeling 9. Discuss Machine Learning algorithms and their implementation 10. Validate Machine Learning algorithms 11. Explain Time Series and its related concepts 12. Perform Text Mining and Sentimental analysis 13. Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Review Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: "I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the "customer of Edureka!". Instructors are knowledge... able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working."
Views: 47716 edureka!
Applying Data Mining Techniques to Computer Systems
 
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Modern computer systems desire several properties, such as high performance, reliability and manageability. To deliver these properties requires a lot of human effort which is costly and error-prone. In order to automate the procedure in development and management, the first step is to analyze and characterize systems by computers. The system data such as source code, development documents, execution traces, access traces, etc. provide a valuable asset for us to target the solution. In the meantime, the huge amount of data, however, renders a tedious and difficult task on managers and developers, and hence the hidden information would be difficult to extract. During this talk, I will present a novel approach to analyze various system data by applying data mining techniques. This approach can effectively obtain useful information hidden in huge amount of system data, and then such information can be exploited for improving system performance, reliability and manageability. Specifically, I have applied different data mining algorithms on different types of system data such as source code and access traces to achieve different goals including automated debugging and system behavior characterization. The results demonstrate that data mining is an effective and promising method to help us solve problems in computer systems.
Views: 73 Microsoft Research
TutORial: Machine Learning and Data Mining with Combinatorial Optimization Algorithms
 
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By Dorit Simona Hochbaum. The dominant algorithms for machine learning tasks fall most often in the realm of AI or continuous optimization of intractable problems. This tutorial presents combinatorial algorithms for machine learning, data mining, and image segmentation that, unlike the majority of existing machine learning methods, utilize pairwise similarities. These algorithms are efficient and reduce the classification problem to a network flow problem on a graph. One of these algorithms addresses the problem of finding a cluster that is as dissimilar as possible from the complement, while having as much similarity as possible within the cluster. These two objectives are combined either as a ratio or with linear weights. This problem is a variant of normalized cut, which is intractable. The problem and the polynomial-time algorithm solving it are called HNC. It is demonstrated here, via an extensive empirical study, that incorporating the use of pairwise similarities improves accuracy of classification and clustering. However, a drawback of the use of similarities is the quadratic rate of growth in the size of the data. A methodology called “sparse computation” has been devised to address and eliminate this quadratic growth. It is demonstrated that the technique of “sparse computation” enables the scalability of similarity-based algorithms to very large-scale data sets while maintaining high levels of accuracy. We demonstrate several applications of variants of HNC for data mining, medical imaging, and image segmentation tasks, including a recent one in which HNC is among the top performing methods in a benchmark for cell identification in calcium imaging movies for neuroscience brain research.
Views: 168 INFORMS
Kruskals Algorithm for Minimum Spanning Tree- Greedy method | Data structures and algorithms
 
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Learn how to find out a minimum spanning tree using Kruskals algorithm in data structure. Jenny’s Lectures CS/IT NET&JRF is a Free YouTube Channel providing Computer Science / Information Technology / Computer-related tutorials including Programming Tutorials, NET & JRF Coaching Videos, Algorithms, GATE Coaching Videos, UGC NET, NTA NET, JRF, BTech, MTech, Ph.D., tips and other helpful videos for Computer Science / Information Technology students to advanced tech theory and computer science lectures, Teaching Computer Science in Informal Space. Learning to teach computer scienceoutside the classroom…. YouTube a top choice for users that want to learn computer programming, but don't have the money or the time to go through a complete college/ Institute / Coaching Centre course. ... Jenny’s Lectures CS/IT NET&JRF is aFree YouTube Channel providing computer-related ... and educate students in science, technology and other subjects. If you have any further questions, query, topic, please don't hesitate to contact me. Please feel free to comment or contact by ([email protected]), if you require any further information. Main Topics: Algorithms, Applied Computer Science, Artificial Intelligence, Coding, Computer Engineering, Computer Networking,Design and Analysis Of Algorithms, Data Structures, Digital Electronics, Object Oriented Programming using C++/Java/Python, Discrete Mathematical Structures, Operating Systems Computer Simulation, Computing, Bit Torrent, Abstract, C, C++, Acrobat, Ada, Pascal, ADABAS, Ad-Aware, Add-in, Add-on, Application Development, Adobe Acrobat, Automatic Data Processing, Adware, Artificial Intelligence, AI, Algorithm, Alphanumeric, Apache, Apache Tomcat, API, Application Programming Interface, Applet, Application, Application Framework, Application Macro, Application Package, Application Program, Application Programmer, Application Server, Application Software, Application Stack, Application Suite, System Administrator, Ada Programming, Architecture, computer software, ASP, Active Server Pages, Assembly, Assembly Language, Audacity, AutoCAD, Autodesk, Auto sketch, Backup, Restore, Backup & Recovery, BASH, BASIC, Beta Version, Binary Tree, Boolean, Boolean Algebra, Boolean AND, Boolean logic, Boolean OR, Boolean value, Binary Search Tree, BST, Bug, Business Software, C Programming Language, Computer Aided Design, Auto CAD, National Testing Agency, NTA,CAD, Callback, Call-by-Reference, Call by reference, Call-by-Value, Call by Value, CD/DVD, Encoding, Mapping, Character, Class, Class Library, ClearCase, ClearQuest, Client, Client-Side, cmd.exe, Cloud computing, Code, Codec, ColdFusion, Command, Command Interpreter, Command.com, Compiler, Animation, Computer Game, Computer Graphics, Computer Science, CONFIG.SYS, Configuration, Copyright, Customer Relationship Management, CRM, CVS, Data, Data Architect, Data Architecture, Data Cleansing, Data Conversion, Data Element, Data Mapping, Data Migration, Data Modeling, Data Processing, Data Scrubbing, Data Structure , Data Transformation, Database Administration, Database Model, Query Language, Database Server, Data log, Debugger, Database Management System, DBMS, Data Definition Language, DDL, Dead Code, Debugger, Decompile, Defragment, Delphi, Design Compiler, Device Driver, Distributed, Data Mart, Data Mining, Data Manipulation Language, DML, DOS, Disk Operating System, Dreamweaver, Drupal, Data Warehouse, Extensible Markup Language, XML, ASCII, Fibonacci , Firefox, Firmware, GUI, Graphical User Interface, LINUX, UNIX, J2EE, Java 2 Platform, Enterprise Edition, Java, Java EE, Java Beans, Java Programming Language, JavaScript, JDBC, Java Database Connectivity, Kernel, Keyboard, Keygen, LAMP, MySQL, Perl, PHP, Python, Logic Programming, Locator, Fusion, Fission, Low-Level Language, Mac OS, Macintosh Operating System, Machine Code, Machine Language, Metadata, Microsoft Access, Microsoft .Net Framework, Microsoft .Net, Microsoft SQL Server, Microsoft Windows, Middleware, MIS, Management Information systems, Module, Mozilla, MS-DOS,Microsoft Disk Operating System, Magic User Interface, MUI, MySQL, Normalization, Numerical, Object-Oriented, Open Source, Solaris, Parallel Processing, Parallel, Patch, Pascal, PDF, Portable Document Format, Postgres, Preemptive, Program, Programming Language, QuickTime, Report Writer, Repository, Rewind, Runtime, Scripting Languages, Script, Search Engine, Software Life-Cycle, VBScript, Virtual Basic Script, Classes, Queues, Stack, B-Tree, Computer Science, Information Technology, IT, CSE
Two Effective Algorithms for Time Series Forecasting
 
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In this talk, Danny Yuan explains intuitively fast Fourier transformation and recurrent neural network. He explores how the concepts play critical roles in time series forecasting. Learn what the tools are, the key concepts associated with them, and why they are useful in time series forecasting. Danny Yuan is a software engineer in Uber. He’s currently working on streaming systems for Uber’s marketplace platform. This video was recorded at QCon.ai 2018: https://bit.ly/2piRtLl For more awesome presentations on innovator and early adopter topics, check InfoQ’s selection of talks from conferences worldwide http://bit.ly/2tm9loz Join a community of over 250 K senior developers by signing up for InfoQ’s weekly Newsletter: https://bit.ly/2wwKVzu
Views: 48026 InfoQ
Hashing and Hash table in data structure and algorithm
 
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This video lecture is produced by S. Saurabh. He is B.Tech from IIT and MS from USA. hashing in data structure hash table hash function hashing in dbms To study interview questions on Linked List watch http://www.youtube.com/playlist?list=PL3D11462114F778D7&feature=view_all To prepare for programming Interview Questions on Binary Trees http://www.youtube.com/playlist?list=PLC3855D81E15BC990&feature=view_all To study programming Interview questions on Stack, Queues, Arrays visit http://www.youtube.com/playlist?list=PL65BCEDD6788C3F27&feature=view_all To watch all Programming Interview Questions visit http://www.youtube.com/playlist?list=PLD629C50E1A85BF84&feature=view_all To learn about Pointers in C visit http://www.youtube.com/playlist?list=PLC68607ACFA43C084&feature=view_all To learn C programming from IITian S.Saurabh visit http://www.youtube.com/playlist?list=PL3C47C530C457BACD&feature=view_all
Views: 331473 saurabhschool
CART Regression Trees Algorithm - Excel part 2
 
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CART, Classification and Regression Trees is a family of Supervised Machine Learning Algorithms. Follow this link for an entire Intro course on Machine Learning using R, did I mention it's FREE: https://www.youtube.com/playlist?list=PLjPbBibKHH18I0mDb_H4uP3egypHIsvMn Also, be sure to check out my channel for over 400 tutorials on Excel, R, Statistics, Machine Learning, basic Math, and more.
Views: 4921 Jalayer Academy
Data mining algorithms with SQL Server and R: part 2 Dejan Sarka
 
01:47:41
Breakout session from DevWeek 2017 DevWeek is the UKs leading conference for professional software developers, architects and analysts. With insights on the latest technolog. Breakout session from DevWeek 2017 DevWeek is the UKs leading conference for professional software developers, architects and analysts. With insights on the latest technolog. Breakout session from DevWeek 2017 Breakout session from DevWeek 2017
Views: 7 Taunya Grillo
Data mining algorithms with SQL Server and R part 1 Dejan Sarka HD
 
01:49:36
Breakout session from DevWeek 2017 DevWeek is the UKs leading conference for professional software developers, architects and analysts. With insights on the latest technolog. Breakout session from DevWeek 2017 DevWeek is the UKs leading conference for professional software developers, architects and analysts. With insights on the latest technolog. Breakout session from DevWeek 2017 Breakout session from DevWeek 2017
Views: 3 Taunya Grillo
Data Structures and Algorithms Complete Tutorial Computer Education for All
 
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Computer Education for all provides complete lectures series on Data Structure and Applications which covers Introduction to Data Structure and its Types including all Steps involves in Data Structures:- Data Structure and algorithm Linear Data Structures and Non-Linear Data Structure on Stack Data Structure on Arrays Data Structure on Queue Data Structure on Linked List Data Structure on Tree Data Structure on Graphs Abstract Data Types Introduction to Algorithms Classifications of Algorithms Algorithm Analysis Algorithm Growth Function Array Operations Two dimensional Arrays Three Dimensional Arrays Multidimensional arrays Matrix operations Operations on linked lists Applications of linked lists Doubly linked lists Introductions to stacks Operations on stack Array based implementation of stack Queue Data Structures Operations on Queues Linked list based implementation of queues Application of Trees Binary Trees Types of Binary Trees Implementation of Binary Trees Binary Tree Traversal Preorder Post order In order Binary Search Tree Introduction to Sorting Analysis of Sorting Algorithms Bubble Sort Selection Sort Insertion Sort Shell Sort Heap Sort Merge Sort Quick Sort Applications of Graphs Matrix representation of Graphs Implementations of Graphs Breadth First Search Topological Sorting Subscribe for More https://www.youtube.com/channel/UCiV37YIYars6msmIQXopIeQ Find us on Facebook: https://web.facebook.com/Computer-Education-for-All-1484033978567298 Java Programming Complete Tutorial for Beginners to Advance | Complete Java Training for all https://youtu.be/gg2PG3TwLx4
Nikunj Oza: "Data-driven Anomaly Detection" | Talks at Google
 
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This talk will describe recent work by the NASA Data Sciences Group on data-driven anomaly detection applied to air traffic control over Los Angeles, Denver, and New York. This data mining approach is designed to discover operationally significant flight anomalies, which were not pre-defined. These methods are complementary to traditional exceedance-based methods, in that they are more likely to yield false alarms, but they are also more likely to find previously-unknown anomalies. We discuss the discoveries that our algorithms have made that exceedance-based methods did not identify. Nikunj Oza is the leader of the Data Sciences Group at NASA Ames Research Center. He also leads a NASA project team which applies data mining to aviation safety. Dr. Ozaąs 40+ research papers represent his research interests which include data mining, machine learning, anomaly detection, and their applications to Aeronautics and Earth Science. He received the Arch T. Colwell Award for co-authoring one of the five most innovative technical papers selected from 3300+ SAE technical papers in 2005. His data mining team received the 2010 NASA Aeronautics Research Mission Directorate Associate Administratorąs Award for best technology achievements by a team. He received his B.S. in Mathematics with Computer Science from MIT in 1994, and M.S. (in 1998) and Ph.D. (in 2001) in Computer Science from the University of California at Berkeley.
Views: 8122 Talks at Google
Data Mining with Weka (3.6: Nearest neighbor)
 
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Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 6: Nearest neighbor http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/YjZnrh https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 47802 WekaMOOC
Predictive Analytics & Machine Learning with SAP HANA
 
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Predictive Analytics & Machine Learning with SAP HANA combines the depth and speed of in-memory analytics with the power of native predictive algorithms. Together with SAP Predictive Analysis for visualization, R's extensive library of statistical and data mining techniques, and the SAP HANA predictive analytic library, you get everything you need to predict the future -- in real-time.
Views: 61034 SAP Technology
What is Data Mining - Data Science Jargon for Beginners
 
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In this video I am going to give a simple and beginner definition of what data mining is in data analytics. The data science industry is very complicated, so I want to define data mining for you today. ► Full Playlist Explaining Data Jargon ( https://www.youtube.com/playlist?list=PL_9qmWdi19yDhnzqVCAhA4ALqDoqjeUOr ) ► http://jobsinthefuture.com/index.php/2017/11/25/what-is-data-mining-data-science-jargon-for-beginners/ Data Mining. This term is nearly self explanatory, but let's dig into it (haha, dig into it, data mining) and define data mining a little more to clarify any details. Data Miners explore large sets of data in order to discover patterns in the sets. Data miners look for patterns in order to define medical, buying habits, food shortages, etc... If you are going into the field of Data Analytics you will most certainly be doing a great deal of data mining. Data mining is a mass scale version of looking through thousands of people's daily biographies. What I mean by "looking through people's biographies" is you will be trying to understand how people are responding the the situation you are researching via data. Let's say your company releases a new drug to the market. This drug has been tested to stop the process of breakdown in joints that often leads to rheumatoid arthritis. Your drug ships out to 10,000 trial patients. Now you have a 10,000 person data set to manage. As the trial operates and the patients report their daily experience with the new drug you are being flooded with data about the drug. It is your job as the data miner to find the patterns and insights in order to accurately determine whether the drug is safe or not, the drug needs improvements, or perhaps the drug is not as effective as the company had hoped. In a nutshell data mining is a data analysts daily routine of researching data sets in order to learn from the data. Don't miss the Full review on Data Analytics defined and how to get a job! --- http://jobsinthefuture.com/index.php/2017/10/21/data-analyst-salary-and-how-to-become-a-data-analyst/ ------- SOCIAL Twitter ► @jobsinthefuture Facebook ►/jobsinthefuture Instagram ►@Jobsinthefuture WHERE I LEARN: (affiliate links) Lynda.com ► http://bit.ly/2rQB2u4 edX.org ► http://fxo.co/4y00 MY FAVORITE GEAR: (affiliate links) Camera ► http://amzn.to/2BWvE9o CamStand ► http://amzn.to/2BWsv9M Compute ► http://amzn.to/2zPeLvs Mouse ► http://amzn.to/2C0T9hq TubeBuddy ► https://www.tubebuddy.com/bengkaiser ► Download the Ultimate Guide Now! ( https://www.getdrip.com/forms/883303253/submissions/new ) Thanks for Supporting Our Channel! DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This help support the channel and allows us to continue to make videos like this. Thank you for the support!
Views: 600 Ben G Kaiser
STATISTICAL APPROACH TO PERFORMANCE COMPARISON OF PREDICTIVE ALGORITHMS
 
38:33
Resistance Spot Welding (RSW) is the dominant process to fabricate body closures and structural components in automotive industry. RSW is a complex process with inconsistent data and highly non-linear relation between process parameters. Several machine learning algorithms have been used to construct predictive models to assess weldability condition of RSW joints. However, to the best of our knowledge, a comprehensive analysis to compare performance of RSW weldability predictive models is lacking. In this investigation, a statistical framework is developed to assess performance superiority (high-accuracy and low-variability) of several machine learning algorithm(s) in RSW applications. First, machine learning algorithms popular in RSW literature are selected and pooled. As our contribution to this pool, a state-of-the-art Deep Neural Net (DNN) algorithm is added. Second, using a ten-fold cross-validation scheme, predictive models are constructed using Advanced High Strength Steel (AHSS) welding data from a major automotive original equipment manufacturer. Third, using Monte Carlo statistical simulation analysis, original and bootstrapped test sets are applied to the pool of constructed models to generate sampling distribution of the estimates, i.e. accuracy measure for each algorithm. Finally, statistical comparative experiments are used to determine the superior predictive algorithm(s) with results that indicate that the DNN model outperforms other models. Our study shows that the DNN model improves accuracy and variability by approximately 19% and 7% on average, respectively. As an improvement for the research for the case of big data scenarios, DATAVIEW a big data infrastructure is used instead of traditional data analytics framework that is developed based on R. DATAVIEW and R scientifically compared by developing full-factorial statistical experiments. Our research indicate that DATAVIEW significantly outperforms R in terms of computational costs and performance efficiency.
Views: 106 Saeed Z.Gavidel
Association Rules شرح
 
52:39
Association Rules شرح - Data Mining
Views: 42474 Emad Tolba
Last Minute Tutorials | Data mining | Introduction | Examples
 
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Please feel free to get in touch with me :) If it helped you, please like my facebook page and don't forget to subscribe to Last Minute Tutorials. Thaaank Youuu. Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ Website: www.lmtutorials.com For any queries or suggestions, kindly mail at: [email protected]
Views: 54909 Last Minute Tutorials
KNN Algorithms Machine Learning in Excel
 
18:29
This is a model used to classify one of the customers in one of the four categories displayed. The KNN algorithm tells us what should be the best option. This way the supermarket can target this customer in a better way.
Views: 326 Business Solutions
Coin Change Problem Number of ways to get total | Dynamic Programming | Algorithms
 
22:50
This video talks about coin change problem using dynamic programming with example. Given infinite supply of coins of different denominations and certain amount. how many ways these coins can be combined to get the given amount. What is Dynamic Programming | How to use it https://www.youtube.com/watch?v=lVR2u9lsxl8 Jenny’s Lectures CS/IT NET&JRF is a Free YouTube Channel providing Computer Science / Information Technology / Computer-related tutorials including Programming Tutorials, NET & JRF Coaching Videos, Algorithms, GATE Coaching Videos, UGC NET, NTA NET, JRF, BTech, MTech, Ph.D., tips and other helpful videos for Computer Science / Information Technology students to advanced tech theory and computer science lectures, Teaching Computer Science in Informal Space. Learning to teach computer science outside the classroom…. YouTube a top choice for users that want to learn computer programming, but don't have the money or the time to go through a complete college/ Institute / Coaching Centre course. ... Jenny’s Lectures CS/IT NET&JRF is a Free YouTube Channel providing computer-related ... and educate students in science, technology and other subjects. If you have any further questions, query, topic, please don't hesitate to contact me. Please feel free to comment or contact by ([email protected]), if you require any further information. Main Topics: Algorithms, Applied Computer Science, Artificial Intelligence, Coding, Computer Engineering, Computer Networking,Design and Analysis Of Algorithms, Data Structures, Digital Electronics, Object Oriented Programming using C++/Java/Python, Discrete Mathematical Structures, Operating Systems Computer Simulation, Computing, Bit Torrent, Abstract, C, C++, Acrobat, Ada, Pascal, ADABAS, Ad-Aware, Add-in, Add-on, Application Development, Adobe Acrobat, Automatic Data Processing, Adware, Artificial Intelligence, AI, Algorithm, Alphanumeric, Apache, Apache Tomcat, API, Application Programming Interface, Applet, Application, Application Framework, Application Macro, Application Package, Application Program, Application Programmer, Application Server, Application Software, Application Stack, Application Suite, System Administrator, Ada Programming, Architecture, computer software, ASP, Active Server Pages, Assembly, Assembly Language, Audacity, AutoCAD, Autodesk, Auto sketch, Backup, Restore, Backup & Recovery, BASH, BASIC, Beta Version, Binary Tree, Boolean, Boolean Algebra, Boolean AND, Boolean logic, Boolean OR, Boolean value, Binary Search Tree, BST, Bug, Business Software, C Programming Language, Computer Aided Design, Auto CAD, National Testing Agency, NTA,CAD, Callback, Call-by-Reference, Call by reference, Call-by-Value, Call by Value, CD/DVD, Encoding, Mapping, Character, Class, Class Library, ClearCase, ClearQuest, Client, Client-Side, cmd.exe, Cloud computing, Code, Codec, ColdFusion, Command, Command Interpreter, Command.com, Compiler, Animation, Computer Game, Computer Graphics, Computer Science, CONFIG.SYS, Configuration, Copyright, Customer Relationship Management, CRM, CVS, Data, Data Architect, Data Architecture, Data Cleansing, Data Conversion, Data Element, Data Mapping, Data Migration, Data Modeling, Data Processing, Data Scrubbing, Data Structure , Data Transformation, Database Administration, Database Model, Query Language, Database Server, Data log, Debugger, Database Management System, DBMS, Data Definition Language, DDL, Dead Code, Debugger, Decompile, Defragment, Delphi, Design Compiler, Device Driver, Distributed, Data Mart, Data Mining, Data Manipulation Language, DML, DOS, Disk Operating System, Dreamweaver, Drupal, Data Warehouse, Extensible Markup Language, XML, ASCII, Fibonacci , Firefox, Firmware, GUI, Graphical User Interface, LINUX, UNIX, J2EE, Java 2 Platform, Enterprise Edition, Java, Java EE, Java Beans, Java Programming Language, JavaScript, JDBC, Java Database Connectivity, Kernel, Keyboard, Keygen, LAMP, MySQL, Perl, PHP, Python, Logic Programming, Locator, Fusion, Fission, Low-Level Language, Mac OS, Macintosh Operating System, Machine Code, Machine Language, Metadata, Microsoft Access, Microsoft .Net Framework, Microsoft .Net, Microsoft SQL Server, Microsoft Windows, Middleware, MIS, Management Information systems, Module, Mozilla, MS-DOS,Microsoft Disk Operating System, Magic User Interface, MUI, MySQL, Normalization, Numerical, Object-Oriented, Open Source, Solaris, Parallel Processing, Parallel, Patch, Pascal, PDF, Portable Document Format, Postgres, Preemptive, Program, Programming Language, QuickTime, Report Writer, Repository, Rewind, Runtime, Scripting Languages, Script, Search Engine, Software Life-Cycle, VBScript, Virtual Basic Script, Classes, Queues, Stack, B-Tree, Computer Science, Information Technology, IT, CSE
Anomaly Detection Approach using Hybrid Algorithm of Data Mining projects
 
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Contact Best Phd Topic Visit us: http://phdtopic.com/
Views: 30 Phdtopic com
Fractional Knapsack Problem using Greedy Method | Example | Data structures and algorithms
 
11:56
Discussed Fractional Knapsack problem using Greedy approach with the help of an example. Jenny’s Lectures CS/IT NET&JRF is a Free YouTube Channel providing Computer Science / Information Technology / Computer-related tutorials including Programming Tutorials, NET & JRF Coaching Videos, Algorithms, GATE Coaching Videos, UGC NET, NTA NET, JRF, BTech, MTech, Ph.D., tips and other helpful videos for Computer Science / Information Technology students to advanced tech theory and computer science lectures, Teaching Computer Science in Informal Space. Learning to teach computer scienceoutside the classroom…. YouTube a top choice for users that want to learn computer programming, but don't have the money or the time to go through a complete college/ Institute / Coaching Centre course. ... Jenny’s Lectures CS/IT NET&JRF is aFree YouTube Channel providing computer-related ... and educate students in science, technology and other subjects. If you have any further questions, query, topic, please don't hesitate to contact me. Please feel free to comment or contact by ([email protected]), if you require any further information. Main Topics: Algorithms, Applied Computer Science, Artificial Intelligence, Coding, Computer Engineering, Computer Networking,Design and Analysis Of Algorithms, Data Structures, Digital Electronics, Object Oriented Programming using C++/Java/Python, Discrete Mathematical Structures, Operating Systems Computer Simulation, Computing, Bit Torrent, Abstract, C, C++, Acrobat, Ada, Pascal, ADABAS, Ad-Aware, Add-in, Add-on, Application Development, Adobe Acrobat, Automatic Data Processing, Adware, Artificial Intelligence, AI, Algorithm, Alphanumeric, Apache, Apache Tomcat, API, Application Programming Interface, Applet, Application, Application Framework, Application Macro, Application Package, Application Program, Application Programmer, Application Server, Application Software, Application Stack, Application Suite, System Administrator, Ada Programming, Architecture, computer software, ASP, Active Server Pages, Assembly, Assembly Language, Audacity, AutoCAD, Autodesk, Auto sketch, Backup, Restore, Backup & Recovery, BASH, BASIC, Beta Version, Binary Tree, Boolean, Boolean Algebra, Boolean AND, Boolean logic, Boolean OR, Boolean value, Binary Search Tree, BST, Bug, Business Software, C Programming Language, Computer Aided Design, Auto CAD, National Testing Agency, NTA,CAD, Callback, Call-by-Reference, Call by reference, Call-by-Value, Call by Value, CD/DVD, Encoding, Mapping, Character, Class, Class Library, ClearCase, ClearQuest, Client, Client-Side, cmd.exe, Cloud computing, Code, Codec, ColdFusion, Command, Command Interpreter, Command.com, Compiler, Animation, Computer Game, Computer Graphics, Computer Science, CONFIG.SYS, Configuration, Copyright, Customer Relationship Management, CRM, CVS, Data, Data Architect, Data Architecture, Data Cleansing, Data Conversion, Data Element, Data Mapping, Data Migration, Data Modeling, Data Processing, Data Scrubbing, Data Structure , Data Transformation, Database Administration, Database Model, Query Language, Database Server, Data log, Debugger, Database Management System, DBMS, Data Definition Language, DDL, Dead Code, Debugger, Decompile, Defragment, Delphi, Design Compiler, Device Driver, Distributed, Data Mart, Data Mining, Data Manipulation Language, DML, DOS, Disk Operating System, Dreamweaver, Drupal, Data Warehouse, Extensible Markup Language, XML, ASCII, Fibonacci , Firefox, Firmware, GUI, Graphical User Interface, LINUX, UNIX, J2EE, Java 2 Platform, Enterprise Edition, Java, Java EE, Java Beans, Java Programming Language, JavaScript, JDBC, Java Database Connectivity, Kernel, Keyboard, Keygen, LAMP, MySQL, Perl, PHP, Python, Logic Programming, Locator, Fusion, Fission, Low-Level Language, Mac OS, Macintosh Operating System, Machine Code, Machine Language, Metadata, Microsoft Access, Microsoft .Net Framework, Microsoft .Net, Microsoft SQL Server, Microsoft Windows, Middleware, MIS, Management Information systems, Module, Mozilla, MS-DOS,Microsoft Disk Operating System, Magic User Interface, MUI, MySQL, Normalization, Numerical, Object-Oriented, Open Source, Solaris, Parallel Processing, Parallel, Patch, Pascal, PDF, Portable Document Format, Postgres, Preemptive, Program, Programming Language, QuickTime, Report Writer, Repository, Rewind, Runtime, Scripting Languages, Script, Search Engine, Software Life-Cycle, VBScript, Virtual Basic Script, Classes, Queues, Stack, B-Tree, Computer Science, Information Technology, IT, CSE
High Dimensional Data
 
57:12
Match the applications to the theorems: (i) Find the variance of traffic volumes in a large network presented as streaming data. (ii) Estimate failure probabilities in a complex systems with many parts. (iii) Group customers into clusters based on what they bought. (a) Projecting high dimensional space to a random low dimensional space scales each vector's length by (roughly) the same factor. (b) A random walk in a high dimensional convex set converges rather fast. (c) Given data points, we can find their best-fit subspace fast. While the theorems are precise, the talk will deal with applications at a high level. Other theorems/applications may be discussed.
Views: 2618 Microsoft Research
Joint Cluster Analysis of Attribute Data and Relationship Data: Problems, Algorithms & Applications
 
01:23:30
Attribute data and relationship data are two principle types of data, representing the intrinsic and extrinsic properties of entities. While attribute data has been the main source of data for cluster analysis, relationship data such as social networks or metabolic networks are becoming increasingly available. In many cases these two data types carry complementary information, which calls for a joint cluster analysis of both data types in order to achieve more natural clusterings. For example, when identifying research communities, relationship data could represent co-author relationships and attribute data could represent the research interests of scientists. Communities could then be identified as clusters of connected scientists with similar research interests. Our introduction of joint cluster analysis is part of a recent, broader trend to consider as much background information as possible in the process of cluster analysis, and in general, in data mining. In this talk, we briefly review related work including constrained clustering, semi-supervised clustering and multi-relational clustering. We then propose the Connected k-Center (CkC) problem, which aims at finding k connected clusters minimizing the radius with respect to the attribute data. We sketch the main ideas of the proof of NP-completeness and present a constant factor approximation algorithm for the CkC problem. Since this algorithm does not scale to large datasets, we have also developed NetScan, a heuristic algorithm that is efficient for large, real databases. We report experimental results from two applications, community identification and document clustering, both based on DBLP data. Our experiments demonstrate that NetScan finds clusters that are more meaningful and accurate than the results of existing algorithms. We conclude the talk with other promising applications and new problems of joint cluster analysis. In particular, we discuss the clustering of gene expression data and the hotspot analysis of crime data as well as a joint cluster analysis problem that does not require the user to specify the number of clusters in advance.
Views: 50 Microsoft Research
Lecture - 34 Data Mining and Knowledge Discovery
 
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Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 135108 nptelhrd
Longest Common Subsequence- Dynamic Programming | Data structures and algorithms
 
25:48
In this video, I have explained the procedure of finding out the longest common subsequence from the strings using dynamic programming(Tabulation method). It is also known as bottom-up approach. Jenny’s Lectures CS/IT NET&JRF is a Free YouTube Channel providing Computer Science / Information Technology / Computer-related tutorials including Programming Tutorials, NET & JRF Coaching Videos, Algorithms, GATE Coaching Videos, UGC NET, NTA NET, JRF, BTech, MTech, Ph.D., tips and other helpful videos for Computer Science / Information Technology students to advanced tech theory and computer science lectures, Teaching Computer Science in Informal Space. Learning to teach computer scienceoutside the classroom…. YouTube a top choice for users that want to learn computer programming, but don't have the money or the time to go through a complete college/ Institute / Coaching Centre course. ... Jenny’s Lectures CS/IT NET&JRF is aFree YouTube Channel providing computer-related ... and educate students in science, technology and other subjects. If you have any further questions, query, topic, please don't hesitate to contact me. Please feel free to comment or contact by ([email protected]), if you require any further information. Main Topics: Algorithms, Applied Computer Science, Artificial Intelligence, Coding, Computer Engineering, Computer Networking,Design and Analysis Of Algorithms, Data Structures, Digital Electronics, Object Oriented Programming using C++/Java/Python, Discrete Mathematical Structures, Operating Systems Computer Simulation, Computing, Bit Torrent, Abstract, C, C++, Acrobat, Ada, Pascal, ADABAS, Ad-Aware, Add-in, Add-on, Application Development, Adobe Acrobat, Automatic Data Processing, Adware, Artificial Intelligence, AI, Algorithm, Alphanumeric, Apache, Apache Tomcat, API, Application Programming Interface, Applet, Application, Application Framework, Application Macro, Application Package, Application Program, Application Programmer, Application Server, Application Software, Application Stack, Application Suite, System Administrator, Ada Programming, Architecture, computer software, ASP, Active Server Pages, Assembly, Assembly Language, Audacity, AutoCAD, Autodesk, Auto sketch, Backup, Restore, Backup & Recovery, BASH, BASIC, Beta Version, Binary Tree, Boolean, Boolean Algebra, Boolean AND, Boolean logic, Boolean OR, Boolean value, Binary Search Tree, BST, Bug, Business Software, C Programming Language, Computer Aided Design, Auto CAD, National Testing Agency, NTA,CAD, Callback, Call-by-Reference, Call by reference, Call-by-Value, Call by Value, CD/DVD, Encoding, Mapping, Character, Class, Class Library, ClearCase, ClearQuest, Client, Client-Side, cmd.exe, Cloud computing, Code, Codec, ColdFusion, Command, Command Interpreter, Command.com, Compiler, Animation, Computer Game, Computer Graphics, Computer Science, CONFIG.SYS, Configuration, Copyright, Customer Relationship Management, CRM, CVS, Data, Data Architect, Data Architecture, Data Cleansing, Data Conversion, Data Element, Data Mapping, Data Migration, Data Modeling, Data Processing, Data Scrubbing, Data Structure , Data Transformation, Database Administration, Database Model, Query Language, Database Server, Data log, Debugger, Database Management System, DBMS, Data Definition Language, DDL, Dead Code, Debugger, Decompile, Defragment, Delphi, Design Compiler, Device Driver, Distributed, Data Mart, Data Mining, Data Manipulation Language, DML, DOS, Disk Operating System, Dreamweaver, Drupal, Data Warehouse, Extensible Markup Language, XML, ASCII, Fibonacci , Firefox, Firmware, GUI, Graphical User Interface, LINUX, UNIX, J2EE, Java 2 Platform, Enterprise Edition, Java, Java EE, Java Beans, Java Programming Language, JavaScript, JDBC, Java Database Connectivity, Kernel, Keyboard, Keygen, LAMP, MySQL, Perl, PHP, Python, Logic Programming, Locator, Fusion, Fission, Low-Level Language, Mac OS, Macintosh Operating System, Machine Code, Machine Language, Metadata, Microsoft Access, Microsoft .Net Framework, Microsoft .Net, Microsoft SQL Server, Microsoft Windows, Middleware, MIS, Management Information systems, Module, Mozilla, MS-DOS,Microsoft Disk Operating System, Magic User Interface, MUI, MySQL, Normalization, Numerical, Object-Oriented, Open Source, Solaris, Parallel Processing, Parallel, Patch, Pascal, PDF, Portable Document Format, Postgres, Preemptive, Program, Programming Language, QuickTime, Report Writer, Repository, Rewind, Runtime, Scripting Languages, Script, Search Engine, Software Life-Cycle, VBScript, Virtual Basic Script, Classes, Queues, Stack, B-Tree, Computer Science, Information Technology, IT, CSE
Machine Learning and the Cloud: Disrupting Threat Detection and Prevention
 
52:46
Mark Russinovich, Chief Technology Officer, Microsoft Azure, Microsoft Machine learning with large data sets gives unprecedented insights and anomaly detection capability. Learn how Microsoft uses the agility and scale of the cloud to protect its infrastructure and customers by applying data mining and machine learning algorithms and security domain learnings to the vast amounts of data and telemetry gathered by its many different systems and services. http://www.rsaconference.com/events/us16
Views: 8503 RSA Conference
Floyd Warshall Algorithm All Pair Shortest Path algorithm | data structures and algorithms
 
31:23
In Today Video I have explained Floyd Warshall Algorithm for finding shortest paths in a weighted graph. It is all pair shortest path graph algorithm. Jenny’s Lectures CS/IT NET&JRF is a Free YouTube Channel providing Computer Science / Information Technology / Computer-related tutorials including Programming Tutorials, NET & JRF Coaching Videos, Algorithms, GATE Coaching Videos, UGC NET, NTA NET, JRF, BTech, MTech, Ph.D., tips and other helpful videos for Computer Science / Information Technology students to advanced tech theory and computer science lectures, Teaching Computer Science in Informal Space. Learning to teach computer scienceoutside the classroom…. YouTube a top choice for users that want to learn computer programming, but don't have the money or the time to go through a complete college/ Institute / Coaching Centre course. ... Jenny’s Lectures CS/IT NET&JRF is aFree YouTube Channel providing computer-related ... and educate students in science, technology and other subjects. If you have any further questions, query, topic, please don't hesitate to contact me. Please feel free to comment or contact by ([email protected]), if you require any further information. Main Topics: Algorithms, Applied Computer Science, Artificial Intelligence, Coding, Computer Engineering, Computer Networking,Design and Analysis Of Algorithms, Data Structures, Digital Electronics, Object Oriented Programming using C++/Java/Python, Discrete Mathematical Structures, Operating Systems Computer Simulation, Computing, Bit Torrent, Abstract, C, C++, Acrobat, Ada, Pascal, ADABAS, Ad-Aware, Add-in, Add-on, Application Development, Adobe Acrobat, Automatic Data Processing, Adware, Artificial Intelligence, AI, Algorithm, Alphanumeric, Apache, Apache Tomcat, API, Application Programming Interface, Applet, Application, Application Framework, Application Macro, Application Package, Application Program, Application Programmer, Application Server, Application Software, Application Stack, Application Suite, System Administrator, Ada Programming, Architecture, computer software, ASP, Active Server Pages, Assembly, Assembly Language, Audacity, AutoCAD, Autodesk, Auto sketch, Backup, Restore, Backup & Recovery, BASH, BASIC, Beta Version, Binary Tree, Boolean, Boolean Algebra, Boolean AND, Boolean logic, Boolean OR, Boolean value, Binary Search Tree, BST, Bug, Business Software, C Programming Language, Computer Aided Design, Auto CAD, National Testing Agency, NTA,CAD, Callback, Call-by-Reference, Call by reference, Call-by-Value, Call by Value, CD/DVD, Encoding, Mapping, Character, Class, Class Library, ClearCase, ClearQuest, Client, Client-Side, cmd.exe, Cloud computing, Code, Codec, ColdFusion, Command, Command Interpreter, Command.com, Compiler, Animation, Computer Game, Computer Graphics, Computer Science, CONFIG.SYS, Configuration, Copyright, Customer Relationship Management, CRM, CVS, Data, Data Architect, Data Architecture, Data Cleansing, Data Conversion, Data Element, Data Mapping, Data Migration, Data Modeling, Data Processing, Data Scrubbing, Data Structure , Data Transformation, Database Administration, Database Model, Query Language, Database Server, Data log, Debugger, Database Management System, DBMS, Data Definition Language, DDL, Dead Code, Debugger, Decompile, Defragment, Delphi, Design Compiler, Device Driver, Distributed, Data Mart, Data Mining, Data Manipulation Language, DML, DOS, Disk Operating System, Dreamweaver, Drupal, Data Warehouse, Extensible Markup Language, XML, ASCII, Fibonacci , Firefox, Firmware, GUI, Graphical User Interface, LINUX, UNIX, J2EE, Java 2 Platform, Enterprise Edition, Java, Java EE, Java Beans, Java Programming Language, JavaScript, JDBC, Java Database Connectivity, Kernel, Keyboard, Keygen, LAMP, MySQL, Perl, PHP, Python, Logic Programming, Locator, Fusion, Fission, Low-Level Language, Mac OS, Macintosh Operating System, Machine Code, Machine Language, Metadata, Microsoft Access, Microsoft .Net Framework, Microsoft .Net, Microsoft SQL Server, Microsoft Windows, Middleware, MIS, Management Information systems, Module, Mozilla, MS-DOS,Microsoft Disk Operating System, Magic User Interface, MUI, MySQL, Normalization, Numerical, Object-Oriented, Open Source, Solaris, Parallel Processing, Parallel, Patch, Pascal, PDF, Portable Document Format, Postgres, Preemptive, Program, Programming Language, QuickTime, Report Writer, Repository, Rewind, Runtime, Scripting Languages, Script, Search Engine, Software Life-Cycle, VBScript, Virtual Basic Script, Classes, Queues, Stack, B-Tree, Computer Science, Information Technology, IT, CSE
Finding All Bridges(cut edge) in a Graph | Data structures and algorithms
 
20:55
In this video I have explained how to find all bridges in a graph using DFS Traversal. Bridge is also known as cut edge. BFS and DFS Video Link: https://www.youtube.com/watch?v=vf-cxgUXcMk Jenny’s Lectures CS/IT NET&JRF is a Free YouTube Channel providing Computer Science / Information Technology / Computer-related tutorials including Programming Tutorials, NET & JRF Coaching Videos, Algorithms, GATE Coaching Videos, UGC NET, NTA NET, JRF, BTech, MTech, Ph.D., tips and other helpful videos for Computer Science / Information Technology students to advanced tech theory and computer science lectures, Teaching Computer Science in Informal Space. Learning to teach computer science outside the classroom…. YouTube a top choice for users that want to learn computer programming, but don't have the money or the time to go through a complete college/ Institute / Coaching Centre course. ... Jenny’s Lectures CS/IT NET&JRF is aFree YouTube Channel providing computer-related ... and educate students in science, technology and other subjects. If you have any further questions, query, topic, please don't hesitate to contact me. Please feel free to comment or contact by ([email protected]), if you require any further information. Main Topics: Algorithms, Applied Computer Science, Artificial Intelligence, Coding, Computer Engineering, Computer Networking,Design and Analysis Of Algorithms, Data Structures, Digital Electronics, Object Oriented Programming using C++/Java/Python, Discrete Mathematical Structures, Operating Systems Computer Simulation, Computing, Bit Torrent, Abstract, C, C++, Acrobat, Ada, Pascal, ADABAS, Ad-Aware, Add-in, Add-on, Application Development, Adobe Acrobat, Automatic Data Processing, Adware, Artificial Intelligence, AI, Algorithm, Alphanumeric, Apache, Apache Tomcat, API, Application Programming Interface, Applet, Application, Application Framework, Application Macro, Application Package, Application Program, Application Programmer, Application Server, Application Software, Application Stack, Application Suite, System Administrator, Ada Programming, Architecture, computer software, ASP, Active Server Pages, Assembly, Assembly Language, Audacity, AutoCAD, Autodesk, Auto sketch, Backup, Restore, Backup & Recovery, BASH, BASIC, Beta Version, Binary Tree, Boolean, Boolean Algebra, Boolean AND, Boolean logic, Boolean OR, Boolean value, Binary Search Tree, BST, Bug, Business Software, C Programming Language, Computer Aided Design, Auto CAD, National Testing Agency, NTA,CAD, Callback, Call-by-Reference, Call by reference, Call-by-Value, Call by Value, CD/DVD, Encoding, Mapping, Character, Class, Class Library, ClearCase, ClearQuest, Client, Client-Side, cmd.exe, Cloud computing, Code, Codec, ColdFusion, Command, Command Interpreter, Command.com, Compiler, Animation, Computer Game, Computer Graphics, Computer Science, CONFIG.SYS, Configuration, Copyright, Customer Relationship Management, CRM, CVS, Data, Data Architect, Data Architecture, Data Cleansing, Data Conversion, Data Element, Data Mapping, Data Migration, Data Modeling, Data Processing, Data Scrubbing, Data Structure , Data Transformation, Database Administration, Database Model, Query Language, Database Server, Data log, Debugger, Database Management System, DBMS, Data Definition Language, DDL, Dead Code, Debugger, Decompile, Defragment, Delphi, Design Compiler, Device Driver, Distributed, Data Mart, Data Mining, Data Manipulation Language, DML, DOS, Disk Operating System, Dreamweaver, Drupal, Data Warehouse, Extensible Markup Language, XML, ASCII, Fibonacci , Firefox, Firmware, GUI, Graphical User Interface, LINUX, UNIX, J2EE, Java 2 Platform, Enterprise Edition, Java, Java EE, Java Beans, Java Programming Language, JavaScript, JDBC, Java Database Connectivity, Kernel, Keyboard, Keygen, LAMP, MySQL, Perl, PHP, Python, Logic Programming, Locator, Fusion, Fission, Low-Level Language, Mac OS, Macintosh Operating System, Machine Code, Machine Language, Metadata, Microsoft Access, Microsoft .Net Framework, Microsoft .Net, Microsoft SQL Server, Microsoft Windows, Middleware, MIS, Management Information systems, Module, Mozilla, MS-DOS,Microsoft Disk Operating System, Magic User Interface, MUI, MySQL, Normalization, Numerical, Object-Oriented, Open Source, Solaris, Parallel Processing, Parallel, Patch, Pascal, PDF, Portable Document Format, Postgres, Preemptive, Program, Programming Language, QuickTime, Report Writer, Repository, Rewind, Runtime, Scripting Languages, Script, Search Engine, Software Life-Cycle, VBScript, Virtual Basic Script, Classes, Queues, Stack, B-Tree, Computer Science, Information Technology, IT, CSE
General Approach to Problem Solving
 
07:52
This video is about using a methodical approach to solving analytical problems. Here are the steps: 1) Problem Definition 2) Representation of Constraints/Objects 3) Strategy/Approach to solving 4) Algorithm 5) Experimentation MIT Lecture: https://youtu.be/L73hY1pBcQI?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi&t=2551 Blog Post: blog.hackerearth.com/how-solve-nondeterministic-polynomial-challenge-problems-programming-contests
Views: 41948 Gaurav Sen
Intelen Stream Web Data mining on Energy Smart Metering (Energy Analytics by Intelen)
 
02:06
Using advanced web data and stream mining Algorithms, we perform on-line stream analysis and smart monitoring of a Boiling Kettle. Every 6 sec, weighted clustering is being performed and statistical indices are analysed on the energy data stream, indicating hidden patters and trends of the device or behavioral use... Our device used Current Cost ENVI meter over cable connection...coming up the wireless one...
Views: 1517 IntelenGroup