Title :
Mining data streams using clustering
Author :
Lu, Yi-Hong ; Huang, Yan
Author_Institution :
Coll. of Inf. Eng., Zhejiang Univ. of Technol., Hangzhou, China
Abstract :
A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Due to this reason, traditional data mining approach is replaced by systems that are able to mine continuous, high-volume, open-ended data streams as they arrive. In this paper, we survey three data stream clustering algorithms, namely clustering data streams using K-means, statistical grid-based, and regression analysis. We compare and contrast these techniques as well.
Keywords :
data mining; pattern clustering; regression analysis; very large databases; K-means algorithm; data mining; data stream clustering; regression analysis algorithm; statistical grid-based algorithm; Clustering algorithms; Communications technology; Data engineering; Data mining; Educational institutions; Monitoring; Multidimensional systems; Real time systems; Regression analysis; Streaming media; Data stream; K-Means algorithm; Minining Data Streams; Regression Analysis; statistical grid-based algorithm;
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
DOI :
10.1109/ICMLC.2005.1527288
