论文信息 - A Clustering Method for Large Spatial Databases

A Clustering Method for Large Spatial Databases

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.

Gabriella Schoier | Giuseppe Borruso

[1] Hans-Peter Kriegel,et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[2] Gabriella Schoier. Blockmodeling Techniques for Web Mining , 2002, COMPSTAT.

[3] Padhraic Smyth,et al. From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[4] Anthony K. H. Tung,et al. Spatial clustering methods in data mining : A survey , 2001 .

[5] Frederik P. Agterberg,et al. Interactive spatial data analysis , 1996 .

[6] Hans-Peter Kriegel,et al. Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications , 1998, Data Mining and Knowledge Discovery.