分区索引最大化算法分析

2009 IEEE International Conference on Fuzzy Systems Pub Date : 2009-10-02 DOI:10.1109/FUZZY.2009.5277353

Kuo-Lung Wu

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引用次数: 1

摘要

在传统的模糊c均值聚类算法中，几乎没有数据点的隶属度值为1。Özdemir和Akarum提出了一种分区索引最大化(PIM)算法，该算法允许数据点可以全部属于一个簇。这个修改可以为每个集群形成一个核心，核心内的数据点的成员值为{0,1}。本文将讨论PIM算法的参数选择问题和鲁棒性。

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An analysis of partition index maximization algorithm

In the traditional fuzzy c-means clustering algorithm, nearly no data points have a membership value one. Özdemir and Akarum proposed a partition index maximization (PIM) algorithm which allows the data points can whole belonging to one cluster. This modification can form a core for each cluster and data points inside the core will have membership value {0,1}. In this paper, we will discuss the parameter selection problems and robust properties of the PIM algorithm.

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2009 IEEE International Conference on Fuzzy Systems

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