An equi-biased k-prototypes algorithm for clustering mixed-type data

Clustering has been recognized as a very important approach for data analysis that partitions the data according to some (dis)similarity criterion. In recent years, the problem of clustering mixed-type data has attracted many researchers. The k-prototypes algorithm is well known for its scalability in this respect. In this paper, the limitations of dissimilarity coefficient used in the k-prototypes algorithm are discussed with some illustrative examples. We propose a new hybrid dissimilarity coefficient for k-prototypes algorithm, which can be applied to the data with numerical, categorical and mixed attributes. Besides retaining the scalability of the k-prototypes algorithm in our method, the dissimilarity functions for either-type attributes are defined on the same scale with respect to their dimensionality, which is very beneficial to improve the efficiency of clustering result. The efficacy of our method is shown by experiments on real and synthetic data sets.