Institutional Repository
Technical University of Crete
Technical University of Crete
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EL
| URI: | http://purl.tuc.gr/dl/dias/17DCC89C-2934-40EF-889D-72322B31E904 | ||
| Year | 2010 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
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| Bibliographic Citation | Nikos 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=87c5599ab55c27b1d1ae1f1f2e76cd6b | ||
| Appears in Collections |
Hierarchical 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.
Copyright © DIAS 2013
