Research on Clustering Method of Improved Glowworm Algorithm Based on Good-Point Set

Abstract

As an important data analysis method in data mining, clustering analysis has been researched extensively and in depth. Aiming at the limitation of K-means clustering algorithm that it is sensitive to the distribution of initial clustering center, Glowworm Swarm Optimization (GSO) Algorithm is introduced to solve clustering problems. Firstly, this paper introduces the basic ideas of GSO algorithm, K-means algorithm, and good-point set and analyzes the feasibility of combining them for clustering optimization. Next, it designs a clustering method of improved GSO algorithm based on good-point set which combines GSO algorithm and classical K-means algorithm together, searches data object space, and provides initial clustering centers for K-means algorithm by means of improved GSO algorithm and thus obtains better clustering results. Major improvement of GSO algorithm is to optimize the initial distribution of glowworm swarm by introducing the theory and method of good-point set. Finally, the new clustering algorithm is applied to UCI data sets of different categories and numbers for clustering test. The advantages of the improved clustering algorithm in terms of sum of squared errors (SSE), clustering accuracy, and robustness are explained through comparison and analysis.