Locally consistent concept factorization for document clustering

Deng Cai, Xiaofei He, Jiawei Han

Research output: Contribution to journalArticle

Abstract

Previous studies have demonstrated that document clustering performance can be improved significantly in lower dimensional linear subspaces. Recently, matrix factorization-based techniques, such as Nonnegative Matrix Factorization (NMF) and Concept Factorization (CF), have yielded impressive results. However, both of them effectively see only the global euclidean geometry, whereas the local manifold geometry is not fully considered. In this paper, we propose a new approach to extract the document concepts which are consistent with the manifold geometry such that each concept corresponds to a connected component. Central to our approach is a graph model which captures the local geometry of the document submanifold. Thus, we call it Locally Consistent Concept Factorization (LCCF). By using the graph Laplacian to smooth the document-to-concept mapping, LCCF can extract concepts with respect to the intrinsic manifold structure and thus documents associated with the same concept can be well clustered. The experimental results on TDT2 and Reuters-21578 have shown that the proposed approach provides a better representation and achieves better clustering results in terms of accuracy and mutual information.

Original languageEnglish (US)
Article number5567104
Pages (from-to)902-913
Number of pages12
JournalIEEE Transactions on Knowledge and Data Engineering
Volume23
Issue number6
DOIs
StatePublished - Mar 15 2011

Keywords

  • Nonnegative matrix factorization
  • clustering.
  • concept factorization
  • graph Laplacian
  • manifold regularization

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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