A New Method on Data Clustering Based on Hybrid K-Harmonic Means and Imperialist Competitive Algorithm

Marjan Abdeyazdan

doi:10.24297/ijct.v10i7.3216

Abstract

Data clustering is one of the commonest data mining techniques. The K-means algorithm is one of the most wellknown clustering algorithms thatare increasingly popular due to the simplicity of implementation and speed of operation. However, its performancecouldbe affected by some issues concerningsensitivity to the initialization and getting stuck in local optima. The K-harmonic means clustering method manages the issue of sensitivity to initialization but the local optimaissue still compromises the algorithm. Particle Swarm Optimization algorithm is a stochastic global optimization technique which is a good solution to the above-mentioned problems. In the present article, the PSOKHM, a hybrid algorithm which draws upon the advantages of both of the algorithms, strives not only to overcome the issue of local optima in KHM but also the slow convergence speed of PSO. In this article, the proposed GSOKHM method, which is a combination of PSO and the evolutionary genetic algorithmwithin PSOKHM,has been positedto enhancethe PSO operation. To carry out this experiment, four real datasets have been employed whose results indicate thatGSOKHMoutperforms PSOKHM.

Highlights

Data clustering is one of the most essential methods in data control and management thatcould partition data into classes accordingto their similar features
The Kharmonic means (KHM), which was proposed in 2002, aims at minimizing the harmonicmeans of all points in a dataset distancing from cluster centers. KM solves the initialization problem, it is still wrestling with the issue of getting stuck at local optima
Our proposed method is comprised of a combination of Particle swarm optimization (PSO) and evolutionary genetic algorithm in PSOKHM to improve PSO operation

Summary

Introduction

Data clustering is one of the most essential methods in data control and management thatcould partition data into classes accordingto their similar features. The Kharmonic means (KHM), which was proposed in 2002, aims at minimizing the harmonicmeans of all points in a dataset distancing from cluster centers. KM solves the initialization problem, it is still wrestling with the issue of getting stuck at local optima. Particle swarm optimization (PSO) is an optimization technique based on population that is inspired by collective and cooperative behavior of bird flocks and fish school. This technique could help KHM to evade local optima trap. Our proposed method is comprised of a combination of PSO and evolutionary genetic algorithm in PSOKHM to improve PSO operation.

K-harmonic means algorithm

The proposed GSOKHM method

Experiments and Results

Results

Summary