{"type":"ScholarlyArticle","title":"Hierarchical Bayesian Clustering for Automatic Text Classification","authors":[{"name":"Makoto Iwayama","slug":"makoto-iwayama","given":"Makoto","family":"Iwayama","affiliation":"Advanced Research Laboratory; Hitachi Ltd . Hatoyama Saitama 350-03 , Japan"},{"name":"Takenobu Tokunaga","slug":"takenobu-tokunaga","given":"Takenobu","family":"Tokunaga","affiliation":"Department of Computer Science Tokyo Institute of Technology Ookayama , Meguro Tokyo 152 , Japan"}],"abstract":"Text classification, the grouping of texts into several clusters, has been used as a means of improving both the efficiency and the effectiveness of text retrieval/categorization. In this paper we propose a hierarchical clustering algorithm that constructs a set of clusters having the maximum Bayesian posterior probability, the probability that the given texts are classified into clusters. We call the algorithm Hierarchical Bayesian Clustering (HBC). The advantages of HBC are experimentally verified from several viewpoints. (1) HBC can re-construct the original clusters more accurately than do other non probabilistic algorithms. (2) When a probabilistic text categorization is extended to a cluster-based one, the use of HBC offers better performance than does the use of non probabilistic algorithms.","doi":null,"url":"https://ijcai.org/proceedings/1995/170","pdf":"https://ijcai.org/proceedings/1995/0170.pdf","markdown":"https://ijcai.org/proceedings/1995/170.md","bibtex":"https://ijcai.org/proceedings/1995/bibtex/170","volume":{"slug":"1995","title":"Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1995","url":"https://ijcai.org/proceedings/1995/","year":1995,"location":"Montréal, Québec, Canada","dates":"","editors":"Chris S. 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