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Extended Fuzzy C-Means Clustering in GIS Environment for Hot Spot Events

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4692))

Abstract

The Extended Fuzzy C-Means (EFCM) algorithm in a Geographic Information System (GIS) is used for identifying the volume clusters as Hot Spot areas, being the data events geo-referenced as points on the geographic map. We have implemented EFCM with the usage of the software tools ESRI/ARCGIS and ESRI/ARCVIEW 3.x and moreover we have made a comparison with the classical Fuzzy C-Means (FCM) algorithm. The application concerns a specific problem of maintenance, executed in the years 2001-2005, over the buildings constructed before 1960 in the city of Cava de’ Tirreni, located in the district of Salerno (Italy).

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Authors and Affiliations

  1. Università degli Studi di Salerno, Dipartimento di Matematica e Informatica, Via Ponte Don Melillo, 84084 Fisciano (Salerno), Italy

    Ferdinando Di Martino & Vincenzo Loia

  2. Università degli Studi di Napoli Federico II, Dipartimento di Costruzioni e Metodi, Matematici in Architettura, Via Monteoliveto 3, 80134 Napoli, Italy

    Salvatore Sessa

Authors
  1. Ferdinando Di Martino
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  2. Vincenzo Loia
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  3. Salvatore Sessa
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Editor information

Bruno Apolloni Robert J. Howlett Lakhmi Jain

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© 2007 Springer-Verlag Berlin Heidelberg

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Di Martino, F., Loia, V., Sessa, S. (2007). Extended Fuzzy C-Means Clustering in GIS Environment for Hot Spot Events. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74819-9_13

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  • DOI: https://doi.org/10.1007/978-3-540-74819-9_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74817-5

  • Online ISBN: 978-3-540-74819-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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