Text Clustering with String Kernels in R

  • Alexandros Karatzoglou
  • Ingo Feinerer
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


We present a package which provides a general framework, including tools and algorithms, for text mining in R using the S4 class system. Using this package and the kernlab R package we explore the use of kernel methods for clustering (e.g., kernel k-means and spectral clustering) on a set of text documents, using string kernels. We compare these methods to a more traditional clustering technique like k-means on a bag of word representation of the text and evaluate the viability of kernel-based methods as a text clustering technique.


Spectral Cluster Recall Rate Text Document Kernel Matrix String Length 
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  1. CANCEDDA, N., GAUSSIER, E., GOUTTE, C. and RENDERS, J.M. (2003): Word-sequence Kernels. Journal of Machine Learning Research, 3, 1059–1082.MathSciNetzbMATHGoogle Scholar
  2. FOWLKES, C., BELONGIE, S., CHUNG, F. and MALIK J. (2004): Spectral Grouping Using the Nystrom Method. Transactions on Pattern Analysis and Machine Intelligence, 26,2, 214–225.CrossRefGoogle Scholar
  3. HERBRICH, R. (2002): Learning Kernel Classifiers Theory and Algorithms. MIT Press.Google Scholar
  4. JOACHIMS, T. (1999): Making Large-scale SVM Learning Practical. In: Advances in Kernel Methods — Support Vector Learning.Google Scholar
  5. JOACHIMS, T. (2002): Learning to Classify Text Using Support Vector Machines: Methods, Theory, and Algorithms. The Kluwer International Series In Engineering And Computer Science. Kluwer Academic Publishers, Boston.CrossRefGoogle Scholar
  6. LEWIS, D. (1997): Reuters-21578 Text Categorization Test Collection.Google Scholar
  7. LODHI, H., SAUNDERS, C., SHAWE-TAYLOR, J., CRISTIANINI, N. and WATKINS, C. (2002): Text Classification Using String Kernels. Journal of Machine Learning Research, 2, 419–444.zbMATHGoogle Scholar
  8. NG, A., JORDAN, M. and WEISS, Y. (2001): On Spectral Clustering: Analysis and an Algorithm. Advances in Neural Information Processing Systems, 14.Google Scholar
  9. R DEVELOPMENT CORE TEAM (2006): R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.Google Scholar
  10. SHI, J. and MALIK, J. (2000): Normalized Cuts and Image Segmentation. Transactions on Pattern Analysis and Machine Intelligence, 22,8, 888–905.CrossRefGoogle Scholar
  11. TEMPLE LANG, D. (2005): Rstem: Interface to Snowball Implementation of Porter’s Word Stemming Algorithm. R Package Version 0.2-0.Google Scholar
  12. VISHWANATHAN, S. and SMOLA, A. (2004): Fast Kernels for String and Tree Matching. In: K. Tsuda, B. Schölkopf and J.P. Vert (Eds.): Kernels and Bioinformatics. MIT Press, Cambridge.Google Scholar
  13. WATKINS, C. (2000): Dynamic Alignment Kernels. In: A.J. Smola, P.L. Bartlett, B. Schölkopf and D. Schuurmans (Eds.): Advances in Large Margin Classifiers. MIT Press, Cambridge, 39–50.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Alexandros Karatzoglou
    • 1
  • Ingo Feinerer
    • 2
  1. 1.Department of Statistics and Probability TheoryTechnische Universität WienWienAustria
  2. 2.Department of Statistics and MathematicsWirtschaftsuniversität WienWienAustria

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