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Clustering Using Difference Criterion of Distortion Ratios

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Knowledge-Based and Intelligent Information and Engineering Systems (KES 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6276))

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Abstract

Clustering using a difference criterion of distortion-ratios on clusters is investigated for data sets with large statistical differences of class data, where K-Means algorithm (KMA) and Learning Vector Quantization (LVQ) cannot necessarily reveal the good performance. After obtaining cluster centers by KMA or LVQ, a split and merge procedure with the difference criterion is executed. Focusing on an interesting data set which is not resolved by KMA or LVQ, some experimental clustering results based on the difference criterion and the split and merge procedure are provided.

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Author information

Authors and Affiliations

  1. Department of Information and Computer Sciences, Nara Women’s University, Nara, 630-8506, Japan

    Fujiki Morii

Authors
  1. Fujiki Morii
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Editor information

Editors and Affiliations

  1. School of Engineering, The Parade, Cardiff University, CF24 3AA, Cardiff, UK

    Rossitza Setchi

  2. Dept. of Computer Science and Software Engineering, BUckingham Building, Lion Terrace, University of Portsmouth, PO1 3HE, Portsmouth, UK

    Ivan Jordanov

  3. KES International, 145-157, St. John Street, EC1V 4PY, London, UK

    Robert J. Howlett

  4. School of Electrical and Information Engineering, University of South Australia, ,, Adelaide, Mawson Lakes Campus, 5095, SA, Australia

    Lakhmi C. Jain

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Morii, F. (2010). Clustering Using Difference Criterion of Distortion Ratios. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science(), vol 6276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15387-7_43

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  • DOI: https://doi.org/10.1007/978-3-642-15387-7_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15386-0

  • Online ISBN: 978-3-642-15387-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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