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Automatic Document Categorization

Interpreting the Perfomance of Clustering Algorithms

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Book cover KI 2003: Advances in Artificial Intelligence (KI 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2821))

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Abstract

Clustering a document collection is the current approach to automatically derive underlying document categories. The categorization performance of a document clustering algorithm can be captured by the F-Measure, which quantifies how close a human-defined categorization has been resembled.

However, a bad F-Measure value tells us nothing about the reason why a clustering algorithm performs poorly. Among several possible explanations the most interesting question is the following: Are the implicit assumptions of the clustering algorithm admissible with respect to a document categorization task?

Though the use of clustering algorithms for document categorization is widely accepted, no foundation or rationale has been stated for this admissibility question. The paper in hand is devoted to this gap. It presents considerations and a measure to quantify the sensibility of a clustering process with regard to geometric distortions of the data space. Along with the method of multidimensional scaling, this measure provides an instrument for accessing a clustering algorithm’s adequacy.

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Authors and Affiliations

  1. Department of Computer Science, Paderborn University, D-33095, Paderborn, Germany

    Benno Stein & Sven Meyer zu Eissen

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  1. Benno Stein
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  2. Sven Meyer zu Eissen
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Editors and Affiliations

  1. HITeC e. V. Universität Hamburg Fachbereich Informatik, Vogt-Kölln-Str. 30, 22527, Hamburg, Germany

    Andreas Günter

  2. Otto-von-Guericke-University of Magdeburg,  

    Rudolf Kruse

  3. Cognitive Systems Laboratory, Department Informatik, Universität Hamburg, 22527, Hamburg, Germany

    Bernd Neumann

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Stein, B., Eissen, S.M.z. (2003). Automatic Document Categorization. In: Günter, A., Kruse, R., Neumann, B. (eds) KI 2003: Advances in Artificial Intelligence. KI 2003. Lecture Notes in Computer Science(), vol 2821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39451-8_19

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  • DOI: https://doi.org/10.1007/978-3-540-39451-8_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20059-8

  • Online ISBN: 978-3-540-39451-8

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