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Comparing Fuzzy Clusterings in High Dimensionality

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Clustering High--Dimensional Data (CHDD 2012)

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Abstract

Due to the specificity of clustering, a problem that is intrinsically ill-posed, there are several approaches to comparing clusterings. Comparison of clusterings obtained in different conditions is often the only affordable evaluation strategy, due to the lack of a ground truth. In this chapter we address a class of dimensionality-independent methods which can be applied in the presence of a high-dimensional input space. Specifically, we review some generalizations of this class of methods to the case of fuzzy clustering, in several variants.

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Authors and Affiliations

  1. DIBRIS – Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi, Università di Genova, Genova, Italy

    Stefano Rovetta & Francesco Masulli

  2. Center for Biotechnology, Temple University, Philadelphia, USA

    Francesco Masulli

Authors
  1. Stefano Rovetta
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  2. Francesco Masulli
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Correspondence to Stefano Rovetta .

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Editors and Affiliations

  1. DIBRIS, University of Genoa DIBRIS, Genoa, Italy

    Francesco Masulli

  2. University of Naples "Parthenope", Naples, Italy

    Alfredo Petrosino

  3. DIBRIS, University of Genoa, Genoa, Italy

    Stefano Rovetta

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Rovetta, S., Masulli, F. (2015). Comparing Fuzzy Clusterings in High Dimensionality. In: Masulli, F., Petrosino, A., Rovetta, S. (eds) Clustering High--Dimensional Data. CHDD 2012. Lecture Notes in Computer Science(), vol 7627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48577-4_4

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  • DOI: https://doi.org/10.1007/978-3-662-48577-4_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-48576-7

  • Online ISBN: 978-3-662-48577-4

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