{"title":"HCC:一种分层共聚类算法","authors":"Jingxuan Li, Tao Li","doi":"10.1145/1835449.1835653","DOIUrl":null,"url":null,"abstract":"In this poster, we develop a novel method, called HCC, for hierarchical co-clustering. HCC brings together two interrelated but distinct themes from clustering: hierarchical clustering and co-clustering. The goal of the former theme is to organize clusters into a hierarchy that facilitates browsing and navigation, while the goal of the latter theme is to cluster different types of data simultaneously by making use of the relationship information. Our initial empirical results are promising and they demonstrate that simultaneously attempting both these goals in a single model leads to improvements over models that focus on a single goal.","PeriodicalId":378368,"journal":{"name":"Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval","volume":"60 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"HCC: a hierarchical co-clustering algorithm\",\"authors\":\"Jingxuan Li, Tao Li\",\"doi\":\"10.1145/1835449.1835653\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this poster, we develop a novel method, called HCC, for hierarchical co-clustering. HCC brings together two interrelated but distinct themes from clustering: hierarchical clustering and co-clustering. The goal of the former theme is to organize clusters into a hierarchy that facilitates browsing and navigation, while the goal of the latter theme is to cluster different types of data simultaneously by making use of the relationship information. Our initial empirical results are promising and they demonstrate that simultaneously attempting both these goals in a single model leads to improvements over models that focus on a single goal.\",\"PeriodicalId\":378368,\"journal\":{\"name\":\"Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval\",\"volume\":\"60 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-07-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1835449.1835653\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1835449.1835653","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this poster, we develop a novel method, called HCC, for hierarchical co-clustering. HCC brings together two interrelated but distinct themes from clustering: hierarchical clustering and co-clustering. The goal of the former theme is to organize clusters into a hierarchy that facilitates browsing and navigation, while the goal of the latter theme is to cluster different types of data simultaneously by making use of the relationship information. Our initial empirical results are promising and they demonstrate that simultaneously attempting both these goals in a single model leads to improvements over models that focus on a single goal.