URI | http://purl.tuc.gr/dl/dias/17DCC89C-2934-40EF-889D-72322B31E904 | - |
Αναγνωριστικό | http://www.scopus.com/inward/record.url?eid=2-s2.0-79960667355&partnerID=40&md5=87c5599ab55c27b1d1ae1f1f2e76cd6b | - |
Γλώσσα | en | - |
Τίτλος | Hierarchical clustering in medical document collections: the BIC-Means method | en |
Δημιουργός | Chourdakis Nikolaos | en |
Δημιουργός | Χουρδακης Νικολαος | el |
Δημιουργός | Argyriou Michail | en |
Δημιουργός | Αργυριου Μιχαηλ | el |
Δημιουργός | Petrakis Evripidis | en |
Δημιουργός | Πετρακης Ευριπιδης | el |
Δημιουργός | Milios, EE | en |
Εκδότης | Elsevier | en |
Περίληψη | 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. | en |
Τύπος | Peer-Reviewed Journal Publication | en |
Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
Ημερομηνία | 2015-10-24 | - |
Ημερομηνία Δημοσίευσης | 2010 | - |
Βιβλιογραφική Αναφορά | 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 | en |