A Central Force Optimization Approach for Data Clustering

Abstract

This research presents a Central Force Optimization (CFO) method for finding the optimal cluster centers in a given dataset. The data clustering is one of the most important research topics of data mining. The objective of the clustering algorithm is to group the similar data objects together and has become an important issue in many fields and applications of sciences and engineering problems like pattern recognition, bioinformatics, machine learning, and etc. Due to the nature of the deterministic and population-based search, CFO can overcome the drawbacks of traditional clustering methods like K-means that easily converge to local optima. A novel and effective clustering based on CFO is proposed in this paper. CFO is an efficient and powerful population-based intelligence algorithm for optimization problems. It is a deterministic nature-inspired metaheuristic, which is based on an analogy to classical particle kinematics in a gravitational field. CFO uses probes as its basic population. The probes are scattered throughout the search space and as time progresses they move slowly towards the probe that has achieved the highest mass or fitness. The performance of the presented algorithm is evaluated on several well-known benchmark datasets and compared with other well-known center-based clustering algorithms. CFO performs well against the other clustering techniques. The comparisons revealed that the proposed algorithm delivers more efficient and effective results than the other clustering techniques.