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A Possibilistic Multivariate Fuzzy c-Means Clustering Algorithm

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Scalable Uncertainty Management (SUM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9858))

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Abstract

In this paper, we present a new possibilistic multivariate fuzzy c-means (PMFCM) clustering algorithm. PMFCM is a combination of multivariate fuzzy c-means (MFCM) and possibilistic fuzzy c-means (PFCM) that produces membership degrees of data objects to each cluster according to each feature and typicality values of data objects to each cluster. In this way, PMFCM produces a multivariate partitioning of a data set detecting clusters with unevenly distributed data over different features. It also reduces the influence of noise and outliers to computation of cluster centers.

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Author information

Authors and Affiliations

  1. Institute of Computer Science, Heinrich-Heine-Universität Düsseldorf, 40225, Düsseldorf, Germany

    Ludmila Himmelspach & Stefan Conrad

Authors
  1. Ludmila Himmelspach
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  2. Stefan Conrad
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Corresponding author

Correspondence to Ludmila Himmelspach .

Editor information

Editors and Affiliations

  1. Cardiff University , Cardiff, United Kingdom

    Steven Schockaert

  2. Télécom ParisTech , Paris, Paris, France

    Pierre Senellart

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© 2016 Springer International Publishing Switzerland

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Himmelspach, L., Conrad, S. (2016). A Possibilistic Multivariate Fuzzy c-Means Clustering Algorithm. In: Schockaert, S., Senellart, P. (eds) Scalable Uncertainty Management. SUM 2016. Lecture Notes in Computer Science(), vol 9858. Springer, Cham. https://doi.org/10.1007/978-3-319-45856-4_24

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  • DOI: https://doi.org/10.1007/978-3-319-45856-4_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45855-7

  • Online ISBN: 978-3-319-45856-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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