An integration of fuzzy association rules and WordNet for document clustering

• Published in: Knowledge and information systems 2011-09, Vol.28 (3), p.687-708
• Main Authors: Chen, Chun-Ling, Tseng, Frank S. C., Liang, Tyne
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Clustering; Clusters; Computer Science; Data mining; Data Mining and Knowledge Discovery; Database Management; Datasets; Documents; Experiments; Fuzzy; Fuzzy logic; Fuzzy set theory; Fuzzy sets; Information Storage and Retrieval; Information Systems and Communication Service; Information Systems Applications (incl.Internet); IT in Business; Labels; Mining; Regular Paper; Semantic relations; Semantics; Studies; Texts; Trouble shooting; Word sense disambiguation
• ISSN: 0219-1377; 0219-3116
• DOI: 10.1007/s10115-010-0364-2

With the rapid growth of text documents, document clustering technique is emerging for efficient document retrieval and better document browsing. Recently, some methods had been proposed to resolve the problems of high dimensionality, scalability, accuracy, and meaningful cluster labels by using frequent itemsets derived from association rule mining for clustering documents. In order to improve the quality of document clustering results, we propose an effective Fuzzy Frequent Itemset-based Document Clustering (F 2 IDC) approach that combines fuzzy association rule mining with the background knowledge embedded in WordNet. A term hierarchy generated from WordNet is applied to discover generalized frequent itemsets as candidate cluster labels for grouping documents. We have conducted experiments to evaluate our approach on Classic4, Re0, R8, and WebKB datasets. Our experimental results show that our proposed approach indeed provide more accurate clustering results than prior influential clustering methods presented in recent literature.