Topic structure mining using temporal co-occurrence

ABSTRACT

This paper proposes a topic structure mining method for document sets that include time stamps. Topic structure mining is a text mining method that uses the graph structure that represents the document pair similarities in the document set. This method yields not only topic extraction from documents and clustering of documents but also extracts the relationship between clusters and the meaning of each document in the cluster. Our method combines temporal co-occurrence with document similarity in constructing the graph structure. We also report evaluation results and the effectiveness of the proposed method.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Search process

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Selection process

General Terms:
Algorithms, Experimentation

Keywords:
PageRank, clustering, graph-based algorithms, topic extraction, topic structure mining