Clustering tagged documents with labeled and unlabeled documents

Chien-Liang Liu*, Wen Hoar Hsaio, Chia Hoang Lee, Chun Hsien Chen

*Corresponding author for this work

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

This study employs our proposed semi-supervised clustering method called Constrained-PLSA to cluster tagged documents with a small amount of labeled documents and uses two data sets for system performance evaluations. The first data set is a document set whose boundaries among the clusters are not clear; while the second one has clear boundaries among clusters. This study employs abstracts of papers and the tags annotated by users to cluster documents. Four combinations of tags and words are used for feature representations. The experimental results indicate that almost all of the methods can benefit from tags. However, unsupervised learning methods fail to function properly in the data set with noisy information, but Constrained-PLSA functions properly. In many real applications, background knowledge is ready, making it appropriate to employ background knowledge in the clustering process to make the learning more fast and effective.

Original languageEnglish
Pages (from-to)596-606
Number of pages11
JournalInformation Processing and Management
Volume49
Issue number3
DOIs
StatePublished - 31 Jan 2013

Keywords

  • Document clustering
  • Semi-supervised clustering
  • Tagged document clustering
  • Text mining

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