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Hierarchical clustering in medical document collections: the BIC-Means method

Chourdakis Nikolaos, Argyriou Michail, Petrakis Evripidis, Milios, EE

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URIhttp://purl.tuc.gr/dl/dias/17DCC89C-2934-40EF-889D-72322B31E904-
Identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-79960667355&partnerID=40&md5=87c5599ab55c27b1d1ae1f1f2e76cd6b-
Languageen-
Title Hierarchical clustering in medical document collections: the BIC-Means methoden
CreatorChourdakis Nikolaosen
CreatorΧουρδακης Νικολαοςel
CreatorArgyriou Michailen
CreatorΑργυριου Μιχαηλel
CreatorPetrakis Evripidisen
CreatorΠετρακης Ευριπιδηςel
CreatorMilios, EEen
PublisherElsevieren
Content SummaryHierarchical clustering of text collections is a key problem in document management and retrieval. In partitional hierarchical clustering, which is more efficient than its agglomerative counterpart, the entire collection is split into clusters and the individual clusters are further split until a heuristicallymotivated termination criterion is met. In this paper, we define the BIC-means algorithm, which applies the Bayesian Information Criterion (BIC) as a domain independent termination criterion for partitional hierarchical clustering. We evaluate the effectiveness of BIC-means in clustering and retrieval on medical document collections and we propose a dynamic version of the BIC-Means algorithm for adapting an existing clustering solution to document additions.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-10-24-
Date of Publication2010-
Bibliographic CitationNikos Hourdakis, Michalis Argyriou, Euripides G.M. Petrakis, Evangelos Milios, "Hierarchical Clustering in Medical Document Collections: the BIC-Means Method" , Journal of Digital Information Management(JDIM), Vol. 8, No. 2, pp. 71-77, April. 2010. http://www.scopus.com/inward/record.url?eid=2-s2.0-79960667355&partnerID=40&md5=87c5599ab55c27b1d1ae1f1f2e76cd6ben

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