Collocation Pattern Analysis

N K Kameswara Rao, G P Saradhi Varma, M. Nagabhushana Rao


Spatial data mining becomes more attractive and significant as more spatial data is built up in spatial databases. Many GIS applications are using spatial patterns that are equal to association rules of a business data mining, i.e., online transaction processing (OLTP). Mining the spatial collocation patterns is a significant spatial data mining job with broad applications. Organizations having large data sets of spatial data need to do certain operations that incorporate methods of analyses and summarization which very much crucial; retailers are finding items frequently bought together to make plan catalogs, store arrangements, and promote products together by using Association rule finding in data mining technique; decision-support systems for getting improved information like transformations and trends that occur in the spatial zones. Particularly, the interpretation on the demonstration of collocation (co-location) pattern and its size change using semantically supported elements is more important to archaeologists, GIS scientists, governments, etc., for analyzing the changing trends in civilization. Many spatial datasets contain occurrence of a collection of Boolean spatial features. Spatial association statistics measure the concentration of an attribute over a space.

Keywords: Spatial data mining, temporal mining, spatial knowledge, collocation, geographic information system


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