{"title":"将用户提供的约束纳入文档聚类","authors":"Yanhua Chen, M. Rege, Ming Dong, Jing Hua","doi":"10.1109/ICDM.2007.67","DOIUrl":null,"url":null,"abstract":"Document clustering without any prior knowledge or background information is a challenging problem. In this paper, we propose SS-NMF: a semi-supervised non- negative matrix factorization framework for document clustering. In SS-NMF, users are able to provide supervision for document clustering in terms of pairwise constraints on a few documents specifying whether they \"must\" or \"cannot\" be clustered together. Through an iterative algorithm, we perform symmetric tri-factorization of the document- document similarity matrix to infer the document clusters. Theoretically, we show that SS-NMF provides a general framework for semi-supervised clustering and that existing approaches can be considered as special cases of SS-NMF. Through extensive experiments conducted on publicly available data sets, we demonstrate the superior performance of SS-NMF for clustering documents.","PeriodicalId":233758,"journal":{"name":"Seventh IEEE International Conference on Data Mining (ICDM 2007)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"41","resultStr":"{\"title\":\"Incorporating User Provided Constraints into Document Clustering\",\"authors\":\"Yanhua Chen, M. Rege, Ming Dong, Jing Hua\",\"doi\":\"10.1109/ICDM.2007.67\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Document clustering without any prior knowledge or background information is a challenging problem. In this paper, we propose SS-NMF: a semi-supervised non- negative matrix factorization framework for document clustering. In SS-NMF, users are able to provide supervision for document clustering in terms of pairwise constraints on a few documents specifying whether they \\\"must\\\" or \\\"cannot\\\" be clustered together. Through an iterative algorithm, we perform symmetric tri-factorization of the document- document similarity matrix to infer the document clusters. Theoretically, we show that SS-NMF provides a general framework for semi-supervised clustering and that existing approaches can be considered as special cases of SS-NMF. Through extensive experiments conducted on publicly available data sets, we demonstrate the superior performance of SS-NMF for clustering documents.\",\"PeriodicalId\":233758,\"journal\":{\"name\":\"Seventh IEEE International Conference on Data Mining (ICDM 2007)\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"41\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Seventh IEEE International Conference on Data Mining (ICDM 2007)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDM.2007.67\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Seventh IEEE International Conference on Data Mining (ICDM 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDM.2007.67","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Incorporating User Provided Constraints into Document Clustering
Document clustering without any prior knowledge or background information is a challenging problem. In this paper, we propose SS-NMF: a semi-supervised non- negative matrix factorization framework for document clustering. In SS-NMF, users are able to provide supervision for document clustering in terms of pairwise constraints on a few documents specifying whether they "must" or "cannot" be clustered together. Through an iterative algorithm, we perform symmetric tri-factorization of the document- document similarity matrix to infer the document clusters. Theoretically, we show that SS-NMF provides a general framework for semi-supervised clustering and that existing approaches can be considered as special cases of SS-NMF. Through extensive experiments conducted on publicly available data sets, we demonstrate the superior performance of SS-NMF for clustering documents.