The Art of Spatial Data Mining

This paper is a literature review of a new algorithm for mining association rules in a spatial database. Spatial data mining, or knowledge discovery in large spatial databases, is the process of extracting implicit knowledge, spatial relations, or other patterns not explicitly stored in spatial databases. Recently, there has been a lot of research in data mining and these studies led to a set of interesting techniques, including methods for mining strong association and dependency rules, attribute-oriented induction for mining characteristic and discriminant rules, etc. Such studies set a foundation and provide some interesting methods for the exploration of highly promising spatial data mining techniques. Based on previous studies on spatial data mining and mining association rules in transaction-based databases, this paper will introduce and study an interesting method for mining strong spatial association rules in large spatial databases [6]. Discovery of spatial association rules may disclose interesting relationship among spatial and non-spatial data in large spatial database and thus it represents a new and promising direction in spatial data warehousing and spatial data mining. Basically the method that will be presented in this paper explores efficient mining of spatial association rules at multiple approximation and abstraction levels. It proposes first to perform less costly, approximate spatial computation to obtain approximate spatial relationships at a high abstraction level and then refine the spatial computation only for those data or predicates whose refined computation may contribute to the discovery of strong association rules. Such two-step spatial mining algorithm facilitates mining strong spatial association rules at multiple concept levels by a top-down, progressive deepening technique [6]. This method is based on the assumption that a user has reasonably good knowledge on what kind of rules he wants to find from the database, and that there exists good knowledge, such as concept or operation hierarchies, for spatial or non-spatial generalization. Such assumptions may rule out naive users and complex spatial databases with poorly understood structures. This paper is related to my current research topics of spatial database, spatial database warehouse modeling and spatial database mining techniques under the supervision of Prof. Adam and Prof. Atluri. I am doing a comprehensive survey right now to try to understand and organize what are the most recent theories and techniques in this area, and the algorithm presented in this paper is one of those that deserve more research efforts, in my opinion. 3 1. Introduction to …