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2-Maximum spanning fuzzy values-based splitting: an iterative clustering algorithm

机译:2-最大跨度基于模糊值的分裂:迭代聚类算法

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Clustering has always been an important research topic that plays an important role in a broad range of applications, from information retrieval to CRM (Customer Relationship Management). It can be described as the process of dividing or grouping a set of data into classes of similar objects. The main goal of this paper is to develop an enhanced fuzzy clustering algorithm which determines the optimal number of clusters based on the detection of the two maximum values to split the worst clusters. For this, we use a new method incrementing the number of clusters in the main loop of an iterative clustering algorithm. Experimental results and comparisons are given to illustrate the performance of the new splitting method compared to the iterative one.
机译:群集始终是一个重要的研究主题,在广泛的应用中,从信息检索到CRM(客户关系管理)中发挥着重要作用。它可以被描述为将一组数据划分或分组成相似对象的类别的过程。本文的主要目标是开发一种增强的模糊聚类算法,该算法基于检测到两个最大值的检测来确定最大簇的最佳簇数。为此,我们使用一种新方法递增迭代聚类算法主循环中的簇数。对实验结果和比较来说明与迭代一个相比的新分裂方法的性能。

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