Abstract
Fuzzy c-means (FCM) is one of the best-known clustering methods to organize the wide variety of datasets automatically and acquire accurate classification, but it has a tendency to fall into local minima. For overcoming these weaknesses, some methods that hybridize PSO and FCM for clustering have been proposed in the literature, and it is demonstrated that these hybrid methods have an improved accuracy over traditional partition clustering approaches, whereas PSO-based clustering methods have poor execution time in comparison to partitional clustering techniques, and the current PSO algorithms require tuning a range of parameters before they are able to find good solutions. Therefore, this paper introduces a hybrid method for fuzzy clustering, named FCM-ELPSO, which aim to deal with these shortcomings. It combines FCM with an improved version of PSO, called ELPSO, which adopts a new enhanced logarithmic inertia weight strategy to provide better balance between exploration and exploitation. This new hybrid method uses PBM(F) index and the objective function value as cluster validity indexes to evaluate the clustering effect. To verify the effectiveness of the algorithm, two types of experiments are performed, including PSO clustering and hybrid clustering. Experiments show that the proposed approach significantly improves convergence speed and the clustering effect.
Highlights
In order to obtain effective information on huge quantities of data quickly and accurately, many methods have been proposed
Many excellent hybrid methods have been proposed for optimal cluster analysis, which do not use PSO as optimization algorithm, such as CRO-fuzzy c-means (FCM) [23] which uses chemical-based metaheuristic obtaining optimal cluster centers for FCM; ETLBO-FCM [24] incorporates elicit teaching learning-based optimization and FCM to overcome the major limitations of FCM; Rahul et al [25] introduced bat optimization to FCM and utilized maxi-min classifier to determine the count of clusters, and the results showed that the clustering accuracy is improved. ese studies have greatly promoted the development of clustering algorithms
Enhanced Logarithmical PSO (ELPSO) takes different inertia weight values during various periods adaptively and provides better balance between exploration and exploitation and avoids falling into local minima quickly, thereby obtaining better solutions. e other contribution of this paper is to propose a new method for the fuzzy clustering problem using hybridization combining FCM with ELPSO, named FCM-ELPSO, which makes use of the merits of both algorithms. is hybrid method introduces ELPSO for training process and uses ELPSO’s global exploration to find a suitable initial clustering prototype for FCM and the local exploration to avoid falling into local optimum and utilizes the fast convergence of FCM to improve the results and convergence time
Summary
In order to obtain effective information on huge quantities of data quickly and accurately, many methods have been proposed. One of the main contributions of this paper is to introduce a new version PSO with enhanced logarithmic decreasing strategy (ELPSO) for clustering Based on this strategy, ELPSO takes different inertia weight values during various periods adaptively and provides better balance between exploration and exploitation and avoids falling into local minima quickly, thereby obtaining better solutions. Is hybrid method introduces ELPSO for training process and uses ELPSO’s global exploration to find a suitable initial clustering prototype for FCM and the local exploration to avoid falling into local optimum and utilizes the fast convergence of FCM to improve the results and convergence time Both clustering methods are tested based on UCI datasets independently, and the results are compared to other PSO-based clustering methods, respectively.
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