World Academy of Science, Engineering and Technology
Published November 23, 2007 | Version 10718
Journal article Open

Categorical Clustering By Converting Associated Information

Description

Lacking an inherent "natural" dissimilarity measure between objects in categorical dataset presents special difficulties in clustering analysis. However, each categorical attributes from a given dataset provides natural probability and information in the sense of Shannon. In this paper, we proposed a novel method which heuristically converts categorical attributes to numerical values by exploiting such associated information. We conduct an experimental study with real-life categorical dataset. The experiment demonstrates the effectiveness of our approach.

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