CLUSTER DISCOVERY TECHNIQUES FOR EXPLORATORY SPATIAL DATA-ANALYSIS

This paper reviews approaches for automated pattern spotting and knowl edge discovery in spatially referenced data. This is an emerging held which to date has received developmental contributions primarily from researchers in statistics and knowledge discovery in databases (KDD). The field of geographical information systems (GIS) has, however, reco gnized its importance as a means for providing more exploratory analys is functionality. Tools based upon automated approaches that identify potentially important relationships in spatial data are essential in G IS in order to effectively deal with the increasing amounts of informa tion being gathered. Clustering techniques are proving to be valuable, but there appears to be a general lack of understanding associated wi th the use and application of various clustering methods in the geogra phic domain. Further, there is little if any recognition of the relati onships between clustering methods. As a result, the development of te chniques known to be problematic or inferior has occurred. This paper presents an overview of clustering methods for exploratory spatial dat a analysis and associated application issues.