{"title":"一类半模糊协同聚类搜索算法","authors":"Rkia Fajr, Ayoub Arafi, Youssef Safi, A. Bouroumi","doi":"10.1109/SITA.2013.6560795","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":145244,"journal":{"name":"2013 8th International Conference on Intelligent Systems: Theories and Applications (SITA)","volume":"31 5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A semi-fuzzy collaborative algorithm for cluster seeking\",\"authors\":\"Rkia Fajr, Ayoub Arafi, Youssef Safi, A. Bouroumi\",\"doi\":\"10.1109/SITA.2013.6560795\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":145244,\"journal\":{\"name\":\"2013 8th International Conference on Intelligent Systems: Theories and Applications (SITA)\",\"volume\":\"31 5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-05-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 8th International Conference on Intelligent Systems: Theories and Applications (SITA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SITA.2013.6560795\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 8th International Conference on Intelligent Systems: Theories and Applications (SITA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SITA.2013.6560795","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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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