Genetic K-Means Clustering Algorithm for Mixed Numeric and Categorical Data Sets

Clustering is one of the major data mining tasks and aims at grouping the data objects into meaningfulclasses (clusters) such that the similarity of objects within clusters is maximized, and the similarity ofobjects from different clusters is minimized. In this paper we present a clustering algorithm based onGenetic k-means paradigm that works well for data with mixed numeric and categorical features. Wepropose a modified description of cluster center to overcome the numeric data only limitation of Genetick-mean algorithm and provide a better characterization of clusters. The performance of this algorithmhas been studied on benchmark data sets