TopCat: Data Mining for Topic Identification in a Text Corpus

  • Chris Clifton
  • Robert Cooley
Conference paper

DOI: 10.1007/978-3-540-48247-5_19

Part of the Lecture Notes in Computer Science book series (LNCS, volume 1704)
Cite this paper as:
Clifton C., Cooley R. (1999) TopCat: Data Mining for Topic Identification in a Text Corpus. In: Żytkow J.M., Rauch J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 1999. Lecture Notes in Computer Science, vol 1704. Springer, Berlin, Heidelberg


TopCat (Topic Categories) is a technique for identifying topics that recur in articles in a text corpus. Natural language processing techniques are used to identify key entities in individual articles, allowing us to represent an article as a set of items. This allows us to view the problem in a database/data mining context: Identifying related groups of items. This paper presents a novel method for identifying related items based on “traditional” data mining techniques. Frequent itemsets are generated from the groups of items, followed by clusters formed with a hypergraph partitioning scheme. We present an evaluation against a manually-categorized “ground truth” news corpus showing this technique is effective in identifying topics in collections of news articles.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Chris Clifton
    • 1
  • Robert Cooley
    • 2
  1. 1.The MITRE CorporationBedfordUSA
  2. 2.University of MinnesotaMinneapolisUSA

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