Hybrid chemical reaction based metaheuristic with fuzzy c-means algorithm for optimal cluster analysis

被引:25
|
作者
Nayak, Janmenjoy [1 ]
Naik, Bighnaraj [2 ]
Behera, Himansu Sekhar [3 ]
Abraham, Ajith [4 ,5 ]
机构
[1] Modern Engn & Management Studies, Dept Comp Sci Engn, Balasore 756056, Odisha, India
[2] Veer Surendra Sai Univ Technol, Dept Comp Applicat, Sambalpur 768018, Odisha, India
[3] Veer Surendra Sai Univ Technol, Dept Comp Sci Engn & Informat Technol, Sambalpur 768018, Odisha, India
[4] MIR Labs, Washington, DC USA
[5] VSB Tech Univ Ostrava, Ctr Excellence IT4Innovat, Ostrava, Czech Republic
关键词
FCM Chemical reaction based optimization; K-means; PSO; IPSO; TLBO; REACTION OPTIMIZATION; NEURAL-NETWORK; PARTICLE SWARM; LOCATION;
D O I
10.1016/j.eswa.2017.02.037
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Hybridization of two or more algorithms has always been a keen interest of research due to the quality of improvement in searching capability. Taking the positive insights of both the algorithms, the developed hybrid algorithm tries to minimize the substantial limitations. Clustering is an unsupervised learning method, which groups the data according to their similar or dissimilar properties. Fuzzy c-means (FCM) is one of the popularly used clustering algorithms and performs better as compared to other clustering techniques such as k-means. However, FCM possesses certain limitations such as premature trapping at local minima and high sensitivity to the cluster center initialization. Taking these issues into consideration, this research proposes a novel hybrid approach of FCM with a recently developed chemical based metaheuristic for obtaining optimal cluster centers. The performance of the proposed approach is compared in terms of cluster fitness values, inter-cluster distance and intra-cluster distance with other evolutionary and swarm optimization based approaches. A rigorous experimentation is simulated and experimental result reveals that the proposed hybrid approach is performing better as compared to other approaches. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:282 / 295
页数:14
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