Candidate groups search for K-harmonic means data clustering

Clustering is a very popular data analysis and data mining technique. K-means is one of the most popular methods for clustering. Although K-mean is easy to implement and works fast in most situations, it suffers from two major drawbacks, sensitivity to initialization and convergence to local optimum. K-harmonic means clustering has been proposed to overcome the first drawback, sensitivity to initialization. In this paper we propose a new algorithm, candidate groups search (CGS), combining with K-harmonic mean to solve clustering problem. Computational results showed CGS does get better performance with less computational time in clustering, especially for large datasets or the number of centers is big.