Data Stream Mining Task
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A Data Stream Mining Task is a sequence data mining task that involves a data stream.
- Context:
- It can be solved by a Data Stream Mining System (that implements a Data Stream Mining algorithm).
- Example(s)
- Counter-Example(s)
- See: Edge Detection.
References
2014
- (Krempl et al., 2014) ⇒ Georg Krempl, Indre Žliobaite, Dariusz Brzeziński, Eyke Hüllermeier, Mark Last, Vincent Lemaire, Tino Noack, Ammar Shaker, Sonja Sievi, Myra Spiliopoulou, and Jerzy Stefanowski. (2014). “Open Challenges for Data Stream Mining Research.” In: ACM SIGKDD Explorations Newsletter Journal, 16(1). doi:10.1145/2674026.2674028
2009
- (Cormode & Hadjieleftheriou) ⇒ Graham Cormode, and Marios Hadjieleftheriou. (2009). “Finding the Frequent Items in Streams of Data.” In: Communications of the ACM, 52(10). doi:10.1145/1562764.1562789
- The frequent items problem is to process a stream of items and find all those which occur more than a given fraction of the time. It is one of the most heavily studied problems in mining data streams, dating back to the 1980s. Many other applications rely directly or indirectly on finding the frequent items, and implementations are in use in large-scale industrial systems.
2005
- (Gaber et al., 2005) ⇒ Mohamed Medhat Gaber, Arkady Zaslavsky, and Shonali Krishnaswamy. (2005). “Mining Data Streams: A Review.” In: ACM SIGMOD Record Journal, 34(2). doi:10.1145/1083784.1083789