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On Model Based Clustering in a Spatial Data Mining Context

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Computational Science and Its Applications – ICCSA 2013 (ICCSA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7974))

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Abstract

In this paper we present the finite mixture models approach to clustering of high dimensional data. The mixture resolving approach to cluster analysis has been addressed in a number of different ways; the underlying assumption is that the patterns to be clustered are drawn from one of several distributions, and the goal is to identify the parameters of each and (perhaps) their number. Finite mixture models allows a flexible approach to the statistical modeling of phenomena characterized by unobserved heterogeneity in different fields of applications. In this analysis we consider the model based clustering on mixture models and compare it with the classical k-means approach. The application regards some aspects of the 218 Municipalities of the region Friuli Venezia Giulia in North-Eastern Italy with data based on the Italian population 2011 Census.

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Author information

Authors and Affiliations

  1. DEAMS - Department of Economic, Business, Mathematic and Statistical Sciences “Bruno de Finetti”, University of Trieste, Via A. Valerio, 4/1, 34127, Trieste, Italy

    Gabriella Schoier & Giuseppe Borruso

Authors
  1. Gabriella Schoier
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  2. Giuseppe Borruso
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Editor information

Editors and Affiliations

  1. L-I.S.U.T. - D.A.P.I.t. Facoltà Ingegneria, Università degli Studi della Basilicata, Viale dell’Ateneo Lucano, 10, 85100, Potenza, Italy

    Beniamino Murgante

  2. Canaanland OTA, Covenant University, Nigeria

    Sanjay Misra

  3. Partimento di Scienze e Tecnologie per LAgricoltura, le Foreste, la Natura e lEnergia, Università degli Studi della Tuscia, Via S. Camillo de Lellis, snc, 01100, Viterbo, Italy

    Maurizio Carlini

  4. Dipartimento di Scienze dell’Ingegneria Civile e dell’Architecttura, Politecnico di Bari, Via Orabona, 4, 70125, Bari, Italy

    Carmelo M. Torre

  5. Quarter 6, Linh Trung, Thu Duc, International University VNU-HCM, Ho Chi Minh City, Vietnam

    Hong-Quang Nguyen

  6. School of Business Systems, Monash University, 3800, Clayton, VIC, Australia

    David Taniar

  7. Department of Intelligent Informatics, Kyushu Sangyo University, 2-3-1 Matsukadai, 813-8503, Higashi-ku, Fukuoka, Japan

    Bernady O. Apduhan

  8. Department of Mathematics and Computer Science, University of Perugia, Via Vanvitelli, 1, 06123, Perugia, Italy

    Osvaldo Gervasi

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Schoier, G., Borruso, G. (2013). On Model Based Clustering in a Spatial Data Mining Context. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2013. ICCSA 2013. Lecture Notes in Computer Science, vol 7974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39649-6_27

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  • DOI: https://doi.org/10.1007/978-3-642-39649-6_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39648-9

  • Online ISBN: 978-3-642-39649-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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