What ' S Spatial about Spatial Datamining : Three Case

Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful, patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more diicult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatial autocorrelation. A popular approach is to apply classical data mining techniques after transforming spatial components into non-spatial components via feature selection. An alternative is to explore new models, new objective functions, and new patterns which are more suitable for spatial data and their unique properties. This chapter investigates techniques in the literature to incorporate spatial components via feature selection, new models, new objective functions, and new patterns. 1. INTRODUCTION Widespread use of spatial databases Gut94, SC01, SCR + 99, Wor95] is leading to an increasing interest in mining interesting and useful, but implicit, spatial patterns Gre00, KAH96, Mar99, RS99, SNM + 95]. Spatial data sets and patterns are abundant in many application domains related to NASA, the National Imagery and Mapping Agency(NIMA), the National Cancer Institute(NCI), and the United States Department of Transportation(USDOT). EEcient tools for extracting information from geo-spatial data are crucial to organizations which make decisions based on large spatial data sets. These organizations are spread across many domains including ecology and environment management, pub