PPT-1 Data Stream Mining

Lesson 1 Bernhard Pfahringer University of Waikato New Zealand 2 Or Why YOU should care about Stream Mining Overview 3 Why is stream mining important How is it different from batch ML. Matvey . Arye. , Princeton/Cloudflare. Albert . Strasheim. , . Cloudflare. Awesome CDN service for websites big & small. Millions of request a second peak. 24 data centers across the globe. Data Analysis. Anthony T. Iannacchione, . engineer. (mining). Stephen J. Tonsor, . biologist. (ecology). Associate Professors, University of Pittsburgh.  . The Question “Can We Conduct Underground Coal Mining and Protect PA Streams?”. Chapter 1. Kirk Scott. Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). 1. Hetal. . Thakkar. , . Nikolay. Laptev, . Hamid. . Mousavi. , . Barzan. . Mozafari. , . Vincenzo. Russo, Carlo Zaniolo. . Computer Science Department UCLA. Data Stream Management Systems (DSMS). a. nd Complex Event Systems. What are different tools available for real-time data mining?. As far as I know there just two tools that are the most well-known in the DS community. These are: (1) VFML, which freely available here http://. (Part 1). Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . slides:. We . would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. (Part . 2). Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . slides:. We . would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). in Robotics Engineering. Blink . Sakulkueakulsuk. D. . Wilking. , and T. . Rofer. , . Realtime. Object Recognition . Using Decision . Tree . Learning, 2005. . http. ://. www.informatik.uni-bremen.de/kogrob/papers/rc05-objectrecognition.pd. 2). Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . slides:. We . would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. TRACDS. Middle East Technical University. October . 31, . 2012. Margaret . H . Dunham, . Michael . Hahsler, Yu . Su, . Sudheer. . Chelluboina. , . and Hadil Shaiba. Computer . Science and . Engineering . Data . Streams. Slides . based on Chapter 4 . in Mining . Data Streams . What is time series data?. Data that is observed or measured at points in time. Timestamp. Fixed periods. e.g. hours, days, months or years. Bamshad Mobasher. DePaul University. 2. From Data to Wisdom. Data. The raw material of information. Information. Data organized and presented by someone. Knowledge. Information read, heard or seen and understood and integrated.