A CLUSTERING ALGORITHM FOR MIXED NUMERIC AND CATEGORICAL DATA

Most of the earlier work on clustering mainly focused on numeric data whoseinherent geometric properties can be exploited to naturally define distance functions be-tween data points. However, data mining applications frequently involve many datasetsthat also consists of mixed numeric and categorical attributes. In this paper we presenta clustering algorithm which is based on the κ-means algorithm. The algorithm clustersobjects with numeric and categorical attributes in a way similar to κ-means. The objectsimilarity measure is derived from both numeric and categorical attributes. When appliedto numeric data, the algorithm is identical to the κ-means. The main result of this paperis to provide a method to update the "cluster centers" of clustering objects described bymixed numeric and categorical attributes in the clustering process to minimize the cluster-ing cost function. The clustering performance of the algorithm is demonstrated with thetwo well known data sets, namely credit approval and abalone