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Refereed Papers

Track: Data Mining

Paper Title:
A New Suffix Tree Similarity Measure for Document Clustering

Authors:

  • Hung Chim (City University of Hong Kong)
  • Xiaotie Deng (City University of Hong Kong)

Abstract:
In this paper, we propose a new similarity measure to compute the pairwise similarity of text-based documents based on suffix tree document model. By applying the new suffix tree similarity measure in Group-average Agglomerative Hierarchical Clustering (GAHC) algorithm, we developed a new suffix tree document clustering algorithm (NSTC). Our experimental results on two standard document clustering benchmark corpus OHSUMED and RCV1 indicate that the new clustering algorithm is a very effective document clustering algorithm. Comparing with the results of traditional keyword tfidf similarity measure in the same GHAC algorithm, NSTC achieved an improvement of 51% on the average of F-measure score. Furthermore, we apply the new clustering algorithm in analyzing the Web documents in online forum communities. A topic oriented clustering algorithm is developed to help people in assessing, classifying and searching the the Web documents in a large forum community.

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