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Association-Based Dissimilarity Measures for Categorical Data: Limitation and Improvement

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Advances in Knowledge Discovery and Data Mining (PAKDD 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3918))

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Abstract

Measuring the similarity for categorical data is a challenging task in data mining due to the poor structure of categorical data. This paper presents a dissimilarity measure for categorical data based on the relations among attributes. This measure not only has the advantage of value variance but also overcomes the limitations of condition the probability-based measure when applied to databases whose attributes are independent. Experiments with 30 databases also showed that the proposed measure boosted the accuracy of Nearest Neighbor classification in comparison with other tested measures.

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Author information

Authors and Affiliations

  1. Japan Advanced Institute of Science and Technology, Tatsunokuchi, Ishikawa, 923-1292, Japan

    Si Quang Le & Tu Bao Ho

  2. LIRMM, Montpellier Cedex 5, France

    Si Quang Le

  3. John von Neumann Institute for Computing, Juelich, Germany

    Le Sy Vinh

  4. American Museum of Natural History, New York, USA

    Le Sy Vinh

Authors
  1. Si Quang Le
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  2. Tu Bao Ho
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  3. Le Sy Vinh
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Editor information

Editors and Affiliations

  1. Nanyang Technological University, Singapore

    Wee-Keong Ng

  2. Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, 153-8505, Tokyo, Japan

    Masaru Kitsuregawa

  3. School of Computer Science and Technology, Heilongjiang University, China

    Jianzhong Li

  4. School of Computer Engineering, Nanyang Technological University, 639798, Singapore, Singapore

    Kuiyu Chang

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© 2006 Springer-Verlag Berlin Heidelberg

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Le, S.Q., Ho, T.B., Vinh, L.S. (2006). Association-Based Dissimilarity Measures for Categorical Data: Limitation and Improvement. In: Ng, WK., Kitsuregawa, M., Li, J., Chang, K. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2006. Lecture Notes in Computer Science(), vol 3918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731139_57

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  • DOI: https://doi.org/10.1007/11731139_57

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33206-0

  • Online ISBN: 978-3-540-33207-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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