Computing Initial points using Density Based Multiscale Data Condensation for Clustering Categorical data
نویسندگان
چکیده
The K-Modes clustering algorithm [1] has shown great promise for clustering large data sets with categorical attributes. K-Mode clustering algorithm suffers from the drawback of choosing random selection of initial points (modes) of the cluster. Different initial points leads to different cluster formations. In this paper Density-based Multiscale Data Condensation [2] approach with hamming distance [1] is used to extract K-initial points. Experiments show that K-modes clustering algorithm using these initial points produce improved and consistent results then the random selection method.
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