一类半模糊协同聚类搜索算法

2013 8th International Conference on Intelligent Systems: Theories and Applications (SITA) Pub Date : 2013-05-08 DOI:10.1109/SITA.2013.6560795

Rkia Fajr, Ayoub Arafi, Youssef Safi, A. Bouroumi

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引用次数: 0

摘要

本文提出了一种半模糊协同算法，用于检测给定数据集中未标记对象的最优聚类数量。该算法基于点间相似性度量，该度量允许集群的检测和创建，以及模糊性度量，该度量允许集群在形成过程中进行协作。该算法还提供了一个代表所有检测到的集群的优化原型矩阵。通过三个测试数据实例验证了该方法的有效性。

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A semi-fuzzy collaborative algorithm for cluster seeking

In this paper, we present a semi-fuzzy collaborative algorithm for detecting the optimal number of clusters in a given data set of unlabeled objects. This algorithm is based on a measure of inter-points similarity that allows the detection and creation of clusters, plus a measure of ambiguity that allows collaboration between clusters during their formation. The algorithm also provides a matrix of optimized prototypes representing all the detected clusters. The performance of the proposed method is demonstrated through three examples of test data.

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2013 8th International Conference on Intelligent Systems: Theories and Applications (SITA)

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