Density cluster methods have elevated computational complexity and are used in spatial analysis for the determination of impact areas. We propose the extended fuzzy c-means (EFCM) algorithm like alternative method because it has three advantages: robustness to noise and outliers, linear computational complexity and automatic determination of the optimal number of clusters. We implement the EFCM algorithm inside a geographic information systems (GIS) for the determination of buffer areas as hypersphere volume prototypes which are circles in the case of bidimensional pattern data. Indeed we have applied this algorithm in the spatial analysis of buffer areas called hotspots, including fire point-events of the Santa Fè district (NM), downloaded from http://www.fs.fed.us/r3/gis/sfe_gis.shtml.

Implementation of the extended fuzzy c-means algorithm in geographic information systems

F. Di Martino;SESSA, SALVATORE
2009

Abstract

Density cluster methods have elevated computational complexity and are used in spatial analysis for the determination of impact areas. We propose the extended fuzzy c-means (EFCM) algorithm like alternative method because it has three advantages: robustness to noise and outliers, linear computational complexity and automatic determination of the optimal number of clusters. We implement the EFCM algorithm inside a geographic information systems (GIS) for the determination of buffer areas as hypersphere volume prototypes which are circles in the case of bidimensional pattern data. Indeed we have applied this algorithm in the spatial analysis of buffer areas called hotspots, including fire point-events of the Santa Fè district (NM), downloaded from http://www.fs.fed.us/r3/gis/sfe_gis.shtml.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/361154
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