K-harmonic means data clustering with simulated annealing heuristic

Abstract Clustering procedures partition a set of objects into clusters such that objects in the same cluster are more similar to each other than objects in different clusters according to some predefined criteria. Clustering is a popular data analysis and data mining technique. Since clustering problem have NP-complete nature, the larger the size of the problem, the harder to find the optimal solution and furthermore, the longer to reach a reasonable results. One of the most used techniques for clustering is based on K-means such that the data is partitioned into K clusters. Although k-means algorithm is easy to implement and works fast in most situations, it suffers from two major drawbacks. One is sensitivity to initialization and the other is convergence to local optima. It is seen from the studies K harmonic means clustering solves the problem of initialization but since its greedy search nature, the second problem; convergence to local optima, still remains. In this paper we develop a new algorithm for solving this problem based on a simulated annealing technique – simulated annealing K-harmonic means clustering (SAKHMC). The experiment results on the Iris and the other well known data, illustrate the robustness of the SAKHMC clustering algorithm.