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Electrical fuzzy C-means: A new heuristic fuzzy clustering algorithm

机译:电气模糊C均值:一种新的启发式模糊聚类算法

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Many heuristic and meta-heuristic algorithms have been successfully applied in the literature to solve the clustering problems. The algorithms have been created for partitioning and classifying a set of data because of two main purposes: at first, for the most compact clusters, second, for the maximum separation between clusters. In this paper, we propose a new heuristic fuzzy clustering algorithm based on electrical rules. The laws of attraction and repulsion of electric charges in an electric field are conducted the same as the target of clustering. The electrical fuzzy C-means (FCM) algorithm proposed in this article use the electrical rules in electric fields and Coulomb’s law to obtain the better and the realest partitioning, having respect to the maximum separation of clusters and the maximum compactness within clusters. Computational results show that our proposed algorithm in comparison with FCM algorithm as a well-known fuzzy clustering algorithm have good performance.
机译:许多启发式和元启发式算法已成功应用于文献中,以解决聚类问题。由于两个主要目的,创建了用于对一组数据进行分区和分类的算法:首先,对于最紧凑的群集,其次,对于群集之间的最大分隔。本文提出了一种基于电气规则的启发式模糊聚类算法。电场中电荷的吸引和排斥定律的执行与聚类的目标相同。本文提出的电模糊C均值(FCM)算法使用了电场中的电规则和库仑定律,从而获得了更好和最真实的分区,同时考虑了群集的最大分离和群集内的最大紧密度。计算结果表明,与FCM算法相比,本文提出的算法具有良好的性能。

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