KES 2007: Knowledge-Based Intelligent Information and Engineering Systems pp 101-107 | Cite as
Extended Fuzzy C-Means Clustering in GIS Environment for Hot Spot Events
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 AnalysisPreview
Unable to display preview. Download preview PDF.
References
- 1.Bezdek, J.C.: Cluster Validity with Fuzzy Sets. IEEE Journal of Cybernetics 8(3), 58–73 (1974)Google Scholar
- 2.Bezdek, J.C.: Numerical Taxonomy with Fuzzy Sets. Journal of Math. Biol. 1, 57–71 (1974)MATHCrossRefGoogle Scholar
- 3.Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)MATHGoogle Scholar
- 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.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.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.Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman & Hall, New York (1986)MATHGoogle Scholar
- 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.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.Wu, K.L., Yang, M.S.: A Fuzzy Validity Index for Fuzzy Clustering. Pattern Recognition Letters 26, 1275–1291 (2005)CrossRefGoogle Scholar