Advertisement

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

  • Ferdinando Di Martino
  • Vincenzo Loia
  • Salvatore Sessa
Part of the Lecture Notes in Computer Science book series (LNCS, 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).

Keywords

Fuzzy C-Means EFCM GIS Hot Spot Event Spatial Analysis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bezdek, J.C.: Cluster Validity with Fuzzy Sets. IEEE Journal of Cybernetics 8(3), 58–73 (1974)Google Scholar
  2. 2.
    Bezdek, J.C.: Numerical Taxonomy with Fuzzy Sets. Journal of Math. Biol. 1, 57–71 (1974)zbMATHCrossRefGoogle Scholar
  3. 3.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)zbMATHGoogle Scholar
  4. 4.
    Fukuyama, Y., Sugeno, M.: A New Method of Choosing the Number of Clusters for the Fuzzy C-Means Method. In: Proceedings of Fifth Fuzzy Systems Symposium, Kobe, Japan, pp. 247–250 (1989)Google Scholar
  5. 5.
    Kaymak, U., Setnes, M.: Fuzzy Clustering with Volume Prototype and Adaptive Cluster Merging. IEEE Trans. on Fuzzy Systems 10(6), 705–712 (2002)CrossRefGoogle Scholar
  6. 6.
    Krishnapuram, R., Kim, J.: Clustering Algorithms Based on Volume Criteria, IEEE Trans. IEEE Trans. on Fuzzy Systems 8(2), 228–236 (2000)CrossRefGoogle Scholar
  7. 7.
    Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman & Hall, New York (1986)zbMATHGoogle Scholar
  8. 8.
    Trauvert, E.: On the Meaning of Dunn’s Partition Coefficient for Fuzzy Clusters. Fuzzy Sets and Systems 25, 217–242 (1988)CrossRefGoogle Scholar
  9. 9.
    Xie, X.L., Beni, I.G.: A Validity Measure for Fuzzy Clustering. IEEE Trans. Pattern Analysis Machine Intell 13, 841–847 (1991)CrossRefGoogle Scholar
  10. 10.
    Wu, K.L., Yang, M.S.: A Fuzzy Validity Index for Fuzzy Clustering. Pattern Recognition Letters 26, 1275–1291 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ferdinando Di Martino
    • 1
  • Vincenzo Loia
    • 1
  • Salvatore Sessa
    • 2
  1. 1.Università degli Studi di Salerno, Dipartimento di Matematica e Informatica, Via Ponte Don Melillo, 84084 Fisciano (Salerno)Italy
  2. 2.Università degli Studi di Napoli Federico II, Dipartimento di Costruzioni e Metodi, Matematici in Architettura, Via Monteoliveto 3, 80134 NapoliItaly

Personalised recommendations