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International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

IEA/AIE 2010: Trends in Applied Intelligent Systems pp 560–569Cite as

Mining Interestingness Measures for String Pattern Mining

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6096))

Abstract

In this paper we present a novel method to detect interesting patterns in strings. A common way to refine results of pattern mining algorithms is using interestingness measures. But the set of appropiate measures is different in each domain and problem. The aim of our research is to obtain a model that classify patterns by interest. The method is based on the application of machine learning algorithms to a generated dataset from factors features. Each dataset row is associated to a factor of a string and contains values of different interestingness measures and contextual information. We also propose a new interestingness measure based on an entropy principle which improves obtained classification results. The proposed method avoids the experts having to configure parameters in order to obtain interesting patterns. We demonstrated the utility of the method by giving example results on real data. The datasets and scripts to reproduce experiments are available on-line.

This work has been partially supported by the SESAAME project, number TIN2008-06582-C03-03, of the MICINN, Spain.

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Author information

Authors and Affiliations

  1. Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, 29071, Málaga, Spain

    Manuel Baena-García & Rafael Morales-Bueno

Authors
  1. Manuel Baena-García
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  2. Rafael Morales-Bueno
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Editor information

Editors and Affiliations

  1. Dept. of Computing and Numerical Analysis, University of Cordoba, Campus Universitario de Rabanales, Einstein Building, 3rd floor, 14071, Cordoba, Spain

    Nicolás García-Pedrajas

  2. Dept. of Computer Science and Artificial Intelligence, ETS de Ingenierias Informática y de Telecomunicación, University of Granada, 18071, Granada, Spain

    Francisco Herrera

  3. School of Computing, University of the West of Scotland, PA1 2BE, Paisley, UK

    Colin Fyfe

  4. Dept. Computer Science and Artificial Intelligence, ETS de Ingenierias Informática y de Telecomunicación, University of Granada, 18071, Granada, Spain

    José Manuel Benítez

  5. Department of Computer Science, Texas State University-San Marcos, 601 University Drive, TX 78666-4616, San Marcos, USA

    Moonis Ali

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© 2010 Springer-Verlag Berlin Heidelberg

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Baena-García, M., Morales-Bueno, R. (2010). Mining Interestingness Measures for String Pattern Mining. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13022-9_56

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  • DOI: https://doi.org/10.1007/978-3-642-13022-9_56

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13021-2

  • Online ISBN: 978-3-642-13022-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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