Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
A framework for projected clustering of high dimensional data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Hierarchical Clustering of Time-Series Data Streams
IEEE Transactions on Knowledge and Data Engineering
Discrete wavelet transform-based time series analysis and mining
ACM Computing Surveys (CSUR)
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This paper describes a novel method for clustering single and multi-dimensional data streams. With incremental computation of the incoming data, our method determines if the cluster formation should change from an initial cluster formation. Four main types of cluster evolutions are studied: cluster appearance, cluster disappearance, cluster splitting, and cluster merging. We present experimental results of our algorithms both in terms of scalability and cluster quality, compared with recent work in this area.