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Clustering categorical data

By Yi Zhang, Ada Wai-chee Fu, Chun Hing Cai and Pheng Ann Heng

Abstract

In this paper we propose two methods to study the problem of clustering categorical data. The first method is based on dynamical system approach. The second method is based on the graph partitioning approach. Dynamical systems approach for clustering categorical data have been studied by some authors [1]. However, the proposed dynamic algorithm cannot guarantee convergence, so that the execution may get into an infinite loop even for very simple data. We define a new conguration updating algorithm for clustering categorical data sets. Let us consider a relational table with k fields, each of which can assume one of a number of possible values. We represent each possible value in each possible field by an abstract node. Let us denote the nodes by vi(i =1;;m). A configuration is an assignment of weight wi for each node vi. The new algorithm is defined as follows. To update the configuration W: create a temporary configuration W 0 with weights w 0 1;:::; w0 m for each weight wui2 W ffor each tuple =fvu1;vu2;;vukg containing vui do x wu1 + + cwui + + wuk: w 0 ui P x

Year: 2000
OAI identifier: oai:CiteSeerX.psu:10.1.1.135.749
Provided by: CiteSeerX
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