clustering binary data streams with k-means
Clustering data streams is an interesting Data Mining problem. This article presents three variants of the K-means algorithm to cluster binary data streams. The variants include On-line K-means, Scalable K-means, and Incremental K-means, a proposed variant introduced that finds higher quality solutions in less time. Higher quality of solutions are obtained with a mean-based initialization and incremental learning. The speedup is achieved through a simplified set of sufficient statistics and operations with sparse matrices. A summary table of clusters is maintained on-line. The K-means variants are compared with respect to quality of results and speed. The proposed algorithms can be used to monitor transactions.
cluster data streaming data mining algorithm scalability incremental quality timing initialization incremental learning speedup statistics operability monitor transaction mean