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International Workshop on Fuzzy Logic and Applications

WILF 2013: Fuzzy Logic and Applications pp 1–14Cite as

What Can Fuzzy Cluster Analysis Contribute to Clustering of High-Dimensional Data?

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8256))

Abstract

Cluster analysis of high-dimensional data has become of special interest in recent years. The term high-dimensional data can refer to a larger number of attributes – 20 or more – as they often occur in database tables. But high-dimensional data can also mean that we have to deal with thousands of attributes as in the context of genomics or proteomics data where thousands of genes or proteins are measured and are considered in some analysis tasks as attributes.

A main reason, why cluster analysis of high-dimensional data is different from clustering low-dimensional data, is the concentration of norm phenomenon, which states more or less that the relative differences between distances between randomly distributed points tend to be more and more similar in higher dimensions.

On the one hand, fuzzy cluster analysis has been shown to be less sensitive to initialisation than, for instance, the classical k-means algorithm. On the other, standard fuzzy clustering is stronger affected by the concentration of norm phenomenon and tends to fail easily in high dimensions. Here we present a review of why fuzzy clustering has special problems with high-dimensional data and how this can be amended by modifying the fuzzifier concept. We also describe a recently introduced approach based on correlation and an attribute selection fuzzy clustering technique that can be applied when clusters can only be found in lower dimensions.

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Author information

Authors and Affiliations

  1. Bioinformatics & Statistics, Helmholtz-Centre for Infection Research, Inhoffenstr. 7, D-38124, Braunschweig, Germany

    Frank Klawonn

  2. Department of Computer Science, Ostfalia University of Applied Sciences, Salzdahlumer Str. 46/48, D-38302, Wolfenbuettel, Germany

    Frank Klawonn

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  1. Frank Klawonn
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Editor information

Editors and Affiliations

  1. DIBRIS, University of Genoa, Via Dodecaneso 35, 16146, Genoa, Italy

    Francesco Masulli

  2. Dept. of Informatics, Systems, and Communication, University of Milano Bicocca, Viale Sarca 336, 20126, Milan, Italy

    Gabriella Pasi

  3. Dept. of Information Systems, Iona College, 710 North Ave, 10801, New Rochelle, NY, USA

    Ronald Yager

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Klawonn, F. (2013). What Can Fuzzy Cluster Analysis Contribute to Clustering of High-Dimensional Data?. In: Masulli, F., Pasi, G., Yager, R. (eds) Fuzzy Logic and Applications. WILF 2013. Lecture Notes in Computer Science(), vol 8256. Springer, Cham. https://doi.org/10.1007/978-3-319-03200-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-03200-9_1

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03199-6

  • Online ISBN: 978-3-319-03200-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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