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A Rough Fuzzy Perspective to Dimensionality Reduction

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Clustering High--Dimensional Data (CHDD 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7627))

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Abstract

Rough set theory and fuzzy logic are mathematical frameworks for granular computing forming a theoretical basis for the treatment of uncertainty in many real–world problems. The focus of rough set theory is on the ambiguity caused by limited discernibility of objects in the domain of discourse; granules are formed as objects and are drawn together by the limited discernibility among them. On the other hand, membership functions of fuzzy sets enables efficient handling of overlapping classes. The hybrid notion of rough fuzzy sets comes from the combination of these two models of uncertainty and helps to exploit, at the same time, properties like coarseness and vagueness. We describe a model of the hybridization of rough and fuzzy sets, that allows for further refinements of rough fuzzy sets and show its application to the task of unsupervised feature selection.

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Authors and Affiliations

  1. Department of Science and Technology, University of Naples Parthenope, Centro Direzionale Isola C4, 80143, Naples, Italy

    Alessio Ferone & Alfredo Petrosino

Authors
  1. Alessio Ferone
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  2. Alfredo Petrosino
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Correspondence to Alessio Ferone .

Editor information

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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Ferone, A., Petrosino, A. (2015). A Rough Fuzzy Perspective to Dimensionality Reduction. 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_9

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  • DOI: https://doi.org/10.1007/978-3-662-48577-4_9

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  • Print ISBN: 978-3-662-48576-7

  • Online ISBN: 978-3-662-48577-4

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