Mining of mixed data with application to catalog marketing

Abstract

Clustering is one of the most popular techniques in data mining. The goal of clustering is to identify distinct groups in a dataset. Many clustering algorithms have been published so far, but often limited to numeric or categorical data. However, most real world data are mixed, numeric and categorical. In this paper, we propose a clustering algorithm CAVE which is based on variance and entropy, and is capable of mining mixed data. The variance is used to measure the similarity of the numeric part of the data. To express the similarity between categorical values, distance hierarchy has been proposed. Accordingly, the similarity of the categorical part is measured based on entropy weighted by the distances in the hierarchies. A new validity index for evaluating the clustering results has also been proposed. The effectiveness of CAVE is demonstrated by a series of experiments on synthetic and real datasets in comparison with that of several traditional clustering algorithms. An application of mining a mixed dataset for customer segmentation and catalog marketing is also presented.