Document Type
Article
Publication Date
4-2006
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Abstract
Analyzing 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.
Repository Citation
Chen, K.,
& Liu, L.
(2006). Detecting the Change of Clustering Structure in Categorical Data Streams. Proceedings of the 2006 SIAM International Conference on Data Mining.
https://corescholar.libraries.wright.edu/knoesis/308
DOI
10.1137/1.9781611972764.49
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Comments
Paper presented at the 2006 Society for Industrial and Applied Mathematics International Conference on Data Mining, Bethesda, MD, April 20-22, 2006.