期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2010
卷号：10
期号：2
页码：246-253
出版社：International Journal of Computer Science and Network Security
摘要：A new algorithm for clustering high-dimensional categorical data is proposed and implemented by us. Our algorithm is parameter-free, fully-automatic and is based on a two-phase iterative procedure. In the first phase, cluster assignments are given, and a new cluster is added to the partition by identifying and splitting a low-quality cluster. Second phase attempts to optimize clusters. This algorithm is parametric to cluster quality in terms of homogeneity. We show how a suitable notion of cluster homogeneity can be defined in the context of high-dimensional categorical data, from which an effective instance of the proposed clustering scheme immediately follows. Our experiments carried out on real data shows that the devised algorithm achieves optimal results in terms of compactness and separation.
关键词：Clustering; high-dimensional categorical data; information search and retrieval
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