Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization

• Published in: Expert systems with applications 2015-10, Vol.42 (17-18), p.6315-6328
• Main Authors: Silva Filho, Telmo M., Pimentel, Bruno A., Souza, Renata M.C.R., Oliveira, Adriano L.I.
• Format: Article
• Language: English
• Subjects: Adaptive weights; Clustering; Expert systems; Fuzzy; Fuzzy c-means; Fuzzy clustering; Heuristic methods; Improved particle swarm optimization; Minima; Swarm intelligence; Tuning
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2015.04.032

•We present two new hybrids of FCM and improved self-adaptive PSO.•The methods are based on the FCM–PSO algorithm.•We use FCM to initialize one particle to achieve better results in less iterations.•The new methods are compared to FCM–PSO using many real and synthetic datasets.•The proposed methods consistently outperform FCM–PSO in three evaluation metrics. Fuzzy clustering has become an important research field with many applications to real world problems. Among fuzzy clustering methods, fuzzy c-means (FCM) is one of the best known for its simplicity and efficiency, although it shows some weaknesses, particularly its tendency to fall into local minima. To tackle this shortcoming, many optimization-based fuzzy clustering methods have been proposed in the literature. Some of these methods are based solely on a metaheuristic optimization, such as particle swarm optimization (PSO) whereas others are hybrid methods that combine a metaheuristic with a traditional partitional clustering method such as FCM. It is demonstrated in the literature that methods that hybridize PSO and FCM for clustering have an improved accuracy over traditional partitional clustering approaches. On the other hand, PSO-based clustering methods have poor execution time in comparison to partitional clustering techniques. Another problem with PSO-based clustering is that the current PSO algorithms require tuning a range of parameters before they are able to find good solutions. In this paper we introduce two hybrid methods for fuzzy clustering that aim to deal with these shortcomings. The methods, referred to as FCM–IDPSO and FCM2–IDPSO, combine FCM with a recent version of PSO, the IDPSO, which adjusts PSO parameters dynamically during execution, aiming to provide better balance between exploration and exploitation, avoiding falling into local minima quickly and thereby obtaining better solutions. Experiments using two synthetic data sets and eight real-world data sets are reported and discussed. The experiments considered the proposed methods as well as some recent PSO-based fuzzy clustering methods. The results show that the methods introduced in this paper provide comparable or in many cases better solutions than the other methods considered in the comparison and were much faster than the other state of the art PSO-based methods.