[1]ZHU Shuwei,ZHOU Zhiping,ZHANG Daowen.K-harmonic means clustering merged with parallel chaotic firefly algorithm[J].CAAI Transactions on Intelligent Systems,2015,10(6):872-880.[doi:10.11992/tis.201505043]
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K-harmonic means clustering merged with parallel chaotic firefly algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 6 Page number: 872-880 Column: 学术论文—机器学习 Public date: 2015-12-25
Title:
K-harmonic means clustering merged with parallel chaotic firefly algorithm
Author(s):
ZHU Shuwei;  ZHOU Zhiping;  ZHANG Daowen
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
Keywords:
K-harmonic means; local optimum; firefly algorithm; clustering; parallel chaotic optimization; chaotic local search; map model; diversity of population
CLC:
TP18
DOI:
10.11992/tis.201505043
Abstract:
The K-harmonic means algorithm(KHM) has the disadvantage of easily falling into a local optimum. To solve this problem, we propose a hybrid KHM based on an improved firefly algorithm(FA). In this paper, we combined raw FA-based searching with parallel chaotic FA-based elaborate searching. In the elaborate searching, we found the current best and second-best solutions using the FA, then we used an improved logistic map model combined with parallel chaotic optimization to search this area in order to enhance the searching ability of the algorithm. Finally, we used the improved FA to optimize the cluster centers obtained by the KHM. Experimental results demonstrate that the proposed algorithm not only had higher search precision for several test functions, but also improved the clustering accuracy and stability of six datasets, effectively avoiding being trapped into a local optimum.
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