Novel fuzzy clustering algorithm for fuzzy data

Vijyant Agarwal
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引用次数: 2

Abstract

This paper presents a new fuzzy clustering algorithm for fuzzy numbers, called the weight fuzzy c-means (WFCM) clustering based on distance function [1]. We first discuss the conventional FCM algorithm for crisp data with brief overview of fuzzy set theory related to the problem at hand and indicate the disparity in the existing approaches of clustering for fuzzy data. In the proposed method, first we converted the fuzzy data matrix into respective weight matrix and then using FCM calculates the membership grade of every fuzzy data. Numerical results show that the presented algorithm performs more robust, logical and superior in performance.
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一种新的模糊数据聚类算法
本文提出了一种新的模糊数的模糊聚类算法,称为基于距离函数的权重模糊c-均值(WFCM)聚类[1]。我们首先讨论了传统的模糊数据聚类算法,简要概述了与手头问题相关的模糊集理论,并指出了现有模糊数据聚类方法的差异。在该方法中,首先将模糊数据矩阵转换为各自的权重矩阵,然后利用FCM计算每个模糊数据的隶属度。数值结果表明,该算法具有较强的鲁棒性、逻辑性和优越的性能。
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