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Presentation
Detecting the Change of Clustering Structure in Categorical Data Streams
Proceedings of the 2006 SIAM International Conference on Data Mining
  • Keke Chen, Wright State University - Main Campus
  • Ling Liu
Document Type
Article
Publication Date
4-1-2006
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.

Comments

Paper presented at the 2006 Society for Industrial and Applied Mathematics International Conference on Data Mining, Bethesda, MD, April 20-22, 2006.

DOI
10.1137/1.9781611972764.49
Citation Information
Keke 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/