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Mathematical Problems in Engineering
Volume 2018 (2018), Article ID 8724084, 8 pages
Research Article

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

1School of Management, Hefei University of Technology, Hefei, Anhui 230009, China
2Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei, Anhui 230009, China
3Anhui Economic Management Institute, Hefei, Anhui 230059, China

Correspondence should be addressed to Zhiwei Ni

Received 26 October 2017; Accepted 8 January 2018; Published 5 March 2018

Academic Editor: Mohammed Nouari

Copyright © 2018 Yaping Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


As an important data analysis method in data mining, clustering analysis has been researched extensively and in depth. Aiming at the limitation of -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, -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 -means algorithm together, searches data object space, and provides initial clustering centers for -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.