Fast mining of spatial collocations

Spatial collocation patterns associate the co-existence of nonspatial features in a spatial neighborhood. An example of such a pattern can associate contaminated water reservoirs with certain deceases in their spatial neighborhood. Previous work on discovering collocation patterns converts neighborhoods of feature instances to itemsets and applies mining techniques for transactional data to discover the patterns. We propose a method that combines the discovery of spatial neighborhoods with the mining process. Our technique is an extension of a spatial join algorithm that operates on multiple inputs and counts long pattern instances. As demonstrated by experimentation, it yields significant performance improvements compared to previous approaches. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications— Data Mining, Spatial Databases and GIS General Terms Algorithms Keywords Collocation Pattern, Spatial Databases

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Xin Zhang, Nikos Mamoulis, David W. Cheung, Yutao Shou
• Keywords: Collocation Pattern, Data Mining, KDD 2004, Spatial, Spatial Neighborhoods