Clustering algorithms with automatic selection of cluster number

In this talk, we study some clustering algorithms with automatic selection of cluster number. Our idea is to introduce a penalty term to the objective function (i) to make the clustering process not sensitive to the initial cluster centers and (ii) to discover cluster structure in a data set. Experimental results on synthetic and real data sets are presented to demonstrate the effectiveness of the proposed algorithm. We also develop the clustering algorithm for categorical data sets and high-dimensional data sets using subspace clustering techniques. Some interesting sub-clusters and subspace clusters in data sets are discovered and reported.