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New Overlap Measure for the Validation of Non-disjoint Partitioning

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Digital Economy. Emerging Technologies and Business Innovation (ICDEc 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 290))

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Abstract

Detecting overlapping groups is a specific challenge which offers relevant solutions for many application domains that require organizing data into non-disjoint clusters. Recently, several methods are proposed in the literature giving different layouts for the overlapping boundaries between clusters. However, the assessment process to evaluate the performance of these methods still a challenging issue to deal with. In fact, existing evaluation measures for overlapping clustering do not take into account the overlap error, local to each data object, while it calculates the whole overlap size relative to all clusters. Therefore, we propose in this work a new external evaluation measure, referred to as Micro-Overlap, able to perform efficient and robust evaluation of overlapping clustering. Experiments on synthetic and real datasets show the performance of the proposed measure compared to existing ones.

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Author information

Authors and Affiliations

  1. ESEN, University of Manouba, Manouba, Tunisia

    Chiheb-Eddine Ben N’Cir

  2. LARODEC, ISG, University of Tunis, Tunis, Tunisia

    Chiheb-Eddine Ben N’Cir & Nadia Essoussi

Authors
  1. Chiheb-Eddine Ben N’Cir
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  2. Nadia Essoussi
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Corresponding author

Correspondence to Chiheb-Eddine Ben N’Cir .

Editor information

Editors and Affiliations

  1. University of Manouba, Manouba, Tunisia

    Rim Jallouli

  2. University of Alberta, Edmonton, Alberta, Canada

    Osmar R. Zaïane

  3. University of Tunis, Tunis, Tunisia

    Mohamed Anis Bach Tobji

  4. University of Manouba, Manouba, Tunisia

    Rym Srarfi Tabbane

  5. University of Twente, Enschede, The Netherlands

    Anton Nijholt

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Ben N’Cir, CE., Essoussi, N. (2017). New Overlap Measure for the Validation of Non-disjoint Partitioning. In: Jallouli, R., Zaïane, O., Bach Tobji, M., Srarfi Tabbane, R., Nijholt, A. (eds) Digital Economy. Emerging Technologies and Business Innovation. ICDEc 2017. Lecture Notes in Business Information Processing, vol 290. Springer, Cham. https://doi.org/10.1007/978-3-319-62737-3_13

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  • DOI: https://doi.org/10.1007/978-3-319-62737-3_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62736-6

  • Online ISBN: 978-3-319-62737-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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