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Vol.21 No.1
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Hierarchic Document Clustering in OPAC

Journal of the Korean Society for Information Management / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2004, v.21 no.1, pp.93-117
https://doi.org/10.3743/KOSIM.2004.21.1.093

(2004). Hierarchic Document Clustering in OPAC. Journal of the Korean Society for Information Management, 21(1), 93-117, https://doi.org/10.3743/KOSIM.2004.21.1.093
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Abstract

This study is to develop a hiararchic clustering model for document classification and browsing in OPAC systems. Two automatic indexing techniques (with and without controlled terms), two term weighting methods (based on term frequency and binary weight), five similarity coefficients (Dice, Jaccard, Pearson, Cosine, and Squared Euclidean), and three hierarchic clustering algorithms (Between Average Linkage, Within Average Linkage, and Complete Linkage method) were tested on the document collection of 175 books and theses on library and information science. The best document clusters resulted from the Between Average Linkage or Complete Linkage method with Jaccard or Dice coefficient on the automatic indexing with controlled terms in binary vector. The clusters from Between Average Linkage with Jaccard has more likely decimal classification structure.

keywords
온라인 목록, 문헌 클러스터링, 계층 클러스터링, 자동분류, 열람, 유사도 계수, OPAC, document clustering, hierarchic clustering, automatic classification, browsing, similarity coefficient

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