Hybrid Fuzzy C-Means Using Bat Optimization and Maxi-Min Distance Classifier

Kumar, Rahul; Dwivedi, Rajesh; Jangam, Ebenezer

doi:10.1007/978-981-13-9942-8_7

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…In recent years, many excellent hybrid methods have been proposed for optimal cluster analysis, which do not use PSO as optimization algorithm, such as CRO-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.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Fuzzy Clustering Method Based on FCM and Enhanced Logarithmical PSO (ELPSO)

Zhang

2020

Computational Intelligence and Neuroscience

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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.

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Section: Introductionmentioning

confidence: 99%