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The proposal of a fuzzy clustering algorithm based on particle swarm

机译:基于粒子群的模糊聚类算法的建议

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This paper proposes the Fuzzy Particle Swarm Clustering (FPSC) algorithm, which is an extension of the crisp data clustering algorithm PSC particularly tailored to deal with fuzzy clusters. The main structural changes of the original PSC algorithm to design FPSC occurred in the selection and evaluation steps of the winner particle, comparing the degree of membership of each object from the database in relation to the particles in the swarm. The FPSC algorithm was applied to eight databases from the literature with the purpose of benchmarking and its performance was compared with that of Fuzzy C-Means and Fuzzy PSO. The results showed that the FPSC algorithm is competitive with the algorithms discussed in this paper.
机译:本文提出了模糊粒子群聚类算法(FPSC),它是针对数据模糊聚类的脆性数据聚类算法PSC的扩展。用于设计FPSC的原始PSC算法的主要结构更改发生在获胜者粒子的选择和评估步骤中,该步骤比较了数据库中每个对象相对于群中粒子的隶属程度。为了进行基准测试,将FPSC算法应用于文献中的八个数据库,并将其性能与Fuzzy C-Means和Fuzzy PSO进行了比较。结果表明,FPSC算法与本文讨论的算法相比具有竞争优势。

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