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New Quality Indexes for Optimal Clustering Model Identification Based on Cross-Domain Approach

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Book cover Advances in Combining Intelligent Methods

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 116 ))

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Abstract

Feature maximization is an alternative measure, as compared to usual distributional measures relying on entropy or on Chi-square metric or vector-based measures, like Euclidean distance or correlation distance. One of the key advantages of this measure taking inspiration both from Galois lattice theory and information retrieval is that it is operational in an incremental mode on traditional classification. In this framework, it does not have the limitations of the aforementioned measures in the case of the processing of highly unbalanced, heterogeneous and highly multidimensional data. We present a new cross-domain application of this measure in the clustering context for setting up new cluster quality indexes that are tolerant to noise and whose efficiency ranges from low to high dimensional data.

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Notes

  1. 1.

    Using p-value highlighting the significance of a feature for a cluster by comparing its contrast to unity contrast would be a potential alternative to the proposed approach. However, this method would introduce unexpected Gaussian smoothing in the process.

  2. 2.

    As regards the principle of the method, this type of selected features inevitably have a contrast greater than 1 in some other cluster(s) (see Eq. 6.3 for details).

  3. 3.

    http://www.research.att.com/~lewis/reuters21578.html.

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Authors and Affiliations

  1. SYNALP Team, LORIA, Bâtiment B, 54506, Vandœuvre-lès-Nancy, France

    Jean-Charles Lamirel

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  1. Jean-Charles Lamirel
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Correspondence to Jean-Charles Lamirel .

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Editors and Affiliations

  1. Sch of Engg, Dept of Comp Engg & Informa, University of Patras Sch of Engg, Dept of Comp Engg & Informa, Patras, Greece

    Ioannis Hatzilygeroudis

  2. Fac of Engg & Compu, dept of Compu, Coventry University, Reader in Perv, Fac of Engg & Compu, dept of Compu, Coventry, United Kingdom

    Vasile Palade

  3. Dept of Edu Sci Early child, Lab of info, Democritus Univer Thrace,Sch of Edu Sci Dept of Edu Sci Early child, Lab of info, Alexandroupolis, Greece

    Jim Prentzas

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Lamirel, JC. (2017). New Quality Indexes for Optimal Clustering Model Identification Based on Cross-Domain Approach. In: Hatzilygeroudis, I., Palade, V., Prentzas, J. (eds) Advances in Combining Intelligent Methods. Intelligent Systems Reference Library, vol 116 . Springer, Cham. https://doi.org/10.1007/978-3-319-46200-4_6

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  • DOI: https://doi.org/10.1007/978-3-319-46200-4_6

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  • Online ISBN: 978-3-319-46200-4

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