{"title":"Learning the Threshold in Hierarchical Agglomerative Clustering","authors":"K. Daniels, C. Giraud-Carrier","doi":"10.1109/ICMLA.2006.33","DOIUrl":null,"url":null,"abstract":"Most partitional clustering algorithms require the number of desired clusters to be set a priori. Not only is this somewhat counter-intuitive, it is also difficult except in the simplest of situations. By contrast, hierarchical clustering may create partitions with varying numbers of clusters. The actual final partition depends on a threshold placed on the similarity measure used. Given a cluster quality metric, one can efficiently discover an appropriate threshold through a form of semi-supervised learning. This paper shows one such solution for complete-link hierarchical agglomerative clustering using the F-measure and a small subset of labeled examples. Empirical evaluation demonstrates promise","PeriodicalId":297071,"journal":{"name":"2006 5th International Conference on Machine Learning and Applications (ICMLA'06)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 5th International Conference on Machine Learning and Applications (ICMLA'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLA.2006.33","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 23
Abstract
Most partitional clustering algorithms require the number of desired clusters to be set a priori. Not only is this somewhat counter-intuitive, it is also difficult except in the simplest of situations. By contrast, hierarchical clustering may create partitions with varying numbers of clusters. The actual final partition depends on a threshold placed on the similarity measure used. Given a cluster quality metric, one can efficiently discover an appropriate threshold through a form of semi-supervised learning. This paper shows one such solution for complete-link hierarchical agglomerative clustering using the F-measure and a small subset of labeled examples. Empirical evaluation demonstrates promise