{"title":"An analysis of partition index maximization algorithm","authors":"Kuo-Lung Wu","doi":"10.1109/FUZZY.2009.5277353","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Conference on Fuzzy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZY.2009.5277353","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
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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