{"title":"用于聚类算法性能度量的类分配算法","authors":"Jie Zhang, Xingsi Xue, Yuping Wang","doi":"10.1109/CIS.2012.31","DOIUrl":null,"url":null,"abstract":"To measure the performance or validity of clustering algorithms, several evaluation values, such as successful rate, successful number and full successful rate are defined. In order to ensure each cluster to at least contain one vector data, and to maximize several proposed evaluation values, two class assignment algorithms are designed. To testify their performance, we employ them to the k-means clustering algorithms.","PeriodicalId":294394,"journal":{"name":"2012 Eighth International Conference on Computational Intelligence and Security","volume":"68 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Class Assignment Algorithms for Performance Measure of Clustering Algorithms\",\"authors\":\"Jie Zhang, Xingsi Xue, Yuping Wang\",\"doi\":\"10.1109/CIS.2012.31\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To measure the performance or validity of clustering algorithms, several evaluation values, such as successful rate, successful number and full successful rate are defined. In order to ensure each cluster to at least contain one vector data, and to maximize several proposed evaluation values, two class assignment algorithms are designed. To testify their performance, we employ them to the k-means clustering algorithms.\",\"PeriodicalId\":294394,\"journal\":{\"name\":\"2012 Eighth International Conference on Computational Intelligence and Security\",\"volume\":\"68 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Eighth International Conference on Computational Intelligence and Security\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIS.2012.31\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Eighth International Conference on Computational Intelligence and Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIS.2012.31","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Class Assignment Algorithms for Performance Measure of Clustering Algorithms
To measure the performance or validity of clustering algorithms, several evaluation values, such as successful rate, successful number and full successful rate are defined. In order to ensure each cluster to at least contain one vector data, and to maximize several proposed evaluation values, two class assignment algorithms are designed. To testify their performance, we employ them to the k-means clustering algorithms.