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Topic-Based Hard Clustering of Documents Using Generative Models

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Foundations of Intelligent Systems (ISMIS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5722))

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Abstract

In this paper, we describe a framework for clustering documents according to their mixtures of topics. The proposed framework combines the expressiveness of generative models for document representation with a properly chosen information-theoretic distance measure to group the documents via an agglomerative hierarchical clustering scheme. The clustering solution obtained at each level of the dendrogram reflects an organization of the documents into sets of topics, while being produced without the effort needed for a soft/fuzzy clustering method. Experimental results obtained on large, real-world collections of documents evidence the effectiveness of our approach in detecting non-overlapping clusters that contain documents sharing similar mixtures of topics.

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Author information

Authors and Affiliations

  1. Dept. of Electronics, Computer and Systems Sciences, University of Calabria, Italy

    Giovanni Ponti & Andrea Tagarelli

Authors
  1. Giovanni Ponti
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  2. Andrea Tagarelli
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Editor information

Editors and Affiliations

  1. Faculty of Informatics and Statistics, University of Economics, W. Churchill Sq. 4, 130 67, Prague 3, Czech Republic

    Jan Rauch

  2. Department of Computer Science, University of North Carolina, NC 27599-3175, Charlotte, USA

    Zbigniew W. Raś

  3. Faculty of Informatics and Statics, University of Economics, W. Churchill Sq. 4, 130 67, Prague, Czech Republic

    Petr Berka

  4. Institute of Software Systems, Tampere University of Technology, P. O. Box 553, 33101, Tampere, Finland

    Tapio Elomaa

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© 2009 Springer-Verlag Berlin Heidelberg

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Ponti, G., Tagarelli, A. (2009). Topic-Based Hard Clustering of Documents Using Generative Models. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds) Foundations of Intelligent Systems. ISMIS 2009. Lecture Notes in Computer Science(), vol 5722. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04125-9_26

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  • DOI: https://doi.org/10.1007/978-3-642-04125-9_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04124-2

  • Online ISBN: 978-3-642-04125-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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