Detecting the Change of Clustering Structure in Categorical Data StreamsProceedings of the 2006 SIAM International Conference on Data Mining
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AbstractAnalyzing clustering structures in data streams can provide critical information for making decision in real time. In this paper, we present a framework for detecting the change of critical clustering structure in categorical data streams. The framework consists of the Hierarchical Entropy Tree structure (HE-Tree) and the extended ACE clustering algorithm. HE-Tree can efficiently capture the entropy property of the categorical data streams and allow us to draw precise clustering information from the data stream for high-quality BkPLots with the extended ACE algorithm.
Citation InformationKeke Chen and Ling Liu. "Detecting the Change of Clustering Structure in Categorical Data Streams" Proceedings of the 2006 SIAM International Conference on Data Mining (2006) ISSN: 9780898716115
Available at: http://works.bepress.com/keke_chen/34/