Data mining refers to a process of analyzing data from different perspectives and summarizing it into useful information that can be used in variety of data centric applications in real time. Geographical Information System (GIS) combined with Data Mining has long being an area of research. GIS is a system that provides information about spatial data. Spatial Data consist of data about real world such as a point on a map represent a place given its latitude and longitude information. Such information of real world constitutes spatial datasets. Data mining in GIS provides a way to analyze this spatial datasets to provide a desired result. Knowledge discovery is a process of extracting implicit knowledge or information from spatial data. Co-location patterns discovery refers to a process of finding subsets of spatial features that are located in close proximity. Spatial Co-location pattern discovery is finding coexistence of non-spatial features in a spatial neighborhood. In this paper we have mined co-location patterns with an approach based on participation index and participation ratio. This technique finds the maximal participation index and uses a clustering algorithm. We have used aprior algorithm method that yields better performance improvement to the algorithm used.

GIS Data Mining Spatial Data Mining Co-location Patterns Participation index participation ratio