Data Mining Methods for the Analysis of LargeGeographic

Spatial data mining, i.e., discovery of interesting, implicit knowledge in spatial databases, is an important task for understanding and use of spatial data-and knowledge-bases. Statistical analysis has been the main method used for analyzing spatial data. Unfortunately, it has a number of weeknesses. In this paper, a number of methods based on knowledge discovery techniques for large databases are presented. This methods may overcome some of the weaknesses of statistical analysis. Our study is focused on ef-cient method for mining strong spatial association rules in geographic information databases. A spatial association rule is a rule indicating certain association relationship among a set of spatial and possibly some non-spatial predicates. For example, a rule \80% of gas stations in rural areas are close to highways" is a spatial association rule. A strong rule indicates that the patterns in the rule have relatively frequent occurrences in the database and strong implication relationships.