Data stream mining
From Wikipedia, the free encyclopedia
Data stream mining is the process of extracting knowledge structures from continuous, rapid data records. Examples of data streams include computer network traffic, phone conversations, ATM transactions, web searches and sensor data.
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[edit] Software for data stream mining
- YALE (YALE (Yet Another Learning Environment)): free open-source software for knowledge discovery, data mining, and machine learning also featuring data stream mining, learning time-varying concepts, and tracking drifting concept (if used in combination with its data stream mining plugin (formerly: concept drift plugin))
[edit] Events on data stream mining
- ACM Symposium on Applied Computing Data Streams Track to be held in conjunction with the 2007 ACM Symposium on Applied Computing (SAC-2007) in Seoul, Korea in March 2007
- IEEE International Workshop on Mining Evolving and Streaming Data (IWMESD 2006) to be held in conjunction with the 2006 IEEE International Conference on Data Mining (ICDM-2006) in Hong Kong in December 2006
- Fourth International Workshop on Knowledge Discovery from Data Streams (IWKDDS) to be held in conjunction with the 17th European Conference on Machine Learning (ECML) and the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) (ECML/PKDD-2006) in Berlin, Germany in September 2006
[edit] Researchers working on data stream mining
- Joao Gama, University of Porto, Portugal
- Ralf Klinkenberg, University of Dortmund, Germany
- Mohamed Medhat Gaber, University of Sydney, Australia
- Olfa Nasraoui, University of Louisville
- Hua-Fu Li, National Chiao-Tung University, Taiwan
- Eyke Hüllermeier, University of Marburg, Germany
