{"title":"一种新的模糊数据聚类算法","authors":"Vijyant Agarwal","doi":"10.1109/IC3.2015.7346671","DOIUrl":null,"url":null,"abstract":"This paper presents a new fuzzy clustering algorithm for fuzzy numbers, called the weight fuzzy c-means (WFCM) clustering based on distance function [1]. We first discuss the conventional FCM algorithm for crisp data with brief overview of fuzzy set theory related to the problem at hand and indicate the disparity in the existing approaches of clustering for fuzzy data. In the proposed method, first we converted the fuzzy data matrix into respective weight matrix and then using FCM calculates the membership grade of every fuzzy data. Numerical results show that the presented algorithm performs more robust, logical and superior in performance.","PeriodicalId":217950,"journal":{"name":"2015 Eighth International Conference on Contemporary Computing (IC3)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Novel fuzzy clustering algorithm for fuzzy data\",\"authors\":\"Vijyant Agarwal\",\"doi\":\"10.1109/IC3.2015.7346671\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a new fuzzy clustering algorithm for fuzzy numbers, called the weight fuzzy c-means (WFCM) clustering based on distance function [1]. We first discuss the conventional FCM algorithm for crisp data with brief overview of fuzzy set theory related to the problem at hand and indicate the disparity in the existing approaches of clustering for fuzzy data. In the proposed method, first we converted the fuzzy data matrix into respective weight matrix and then using FCM calculates the membership grade of every fuzzy data. Numerical results show that the presented algorithm performs more robust, logical and superior in performance.\",\"PeriodicalId\":217950,\"journal\":{\"name\":\"2015 Eighth International Conference on Contemporary Computing (IC3)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 Eighth International Conference on Contemporary Computing (IC3)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IC3.2015.7346671\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Eighth International Conference on Contemporary Computing (IC3)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IC3.2015.7346671","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presents a new fuzzy clustering algorithm for fuzzy numbers, called the weight fuzzy c-means (WFCM) clustering based on distance function [1]. We first discuss the conventional FCM algorithm for crisp data with brief overview of fuzzy set theory related to the problem at hand and indicate the disparity in the existing approaches of clustering for fuzzy data. In the proposed method, first we converted the fuzzy data matrix into respective weight matrix and then using FCM calculates the membership grade of every fuzzy data. Numerical results show that the presented algorithm performs more robust, logical and superior in performance.