Analyzing clustering structures in data streams can provide critical information for making decision in realtime. 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. HETree can efficiently capture the entropy property of the categorical data streams and allow us to draw precise clustering information from the data stream for highquality BkPLots with the extended ACE algorithm.
Based on the BKPlot method, we proposed to detect the change of clustering structure in data streams REF , which covers the basic concepts of the HE-Tree.
Detecting the Change of Clustering Structure in Categorical Data Streams
In Proceedings of SDM
Computer Science