Abstract
The rapid developments in the availability and access to spatially referenced information in a variety of areas, has induced the need for better analysis techniques to understand the various phenomena. In particular spatial clustering algorithms which groups similar spatial objects into classes can be used for the identification of areas sharing common characteristics. The aim of this paper is to present a density-based algorithm for the discover of clusters in large spatial data set which is a modification of a recently proposed algorithm.This is applied to a real data set related to homogeneous agricultural environments. © Springer-Verlag 2004.
Cite
CITATION STYLE
Schoier, G., & Borruso, G. (2004). A Clustering method for large spatial databases. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3044, 1089–1095. https://doi.org/10.1007/978-3-540-24709-8_114
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