A Survey on Co-location and Segregation Patterns Discovery from Spatial Data.

Miss Priya S. Shejwal, Prof.Mrs. J. R. Mankar

ABSTRACT Spatial data mining is the extraction of spatial patterns, spatial relations and implicit knowledge, that are not explicitly stored in databases. In spatial domains, interaction between features generates two types of interaction patterns. A positive interaction (aggregation) brings a subset of features close to each other whereas a negative interaction (inhibition) results in subsets of features segregating from each other. Co-location patterns, intended to represents positive interactions, have been defined as subsets of Boolean spatial features whose instances are often seen to be located at close spatial proximity. Segregation patterns, representing negative interactions, can be defined as subsets of Boolean spatial features whose instances are infrequently seen to be located at close spatial proximity. In this paper, different approaches to discover co-location and segregation patterns like Rule based approach, event centric model approach and approaches for finding complex patterns are discussed. There are several measures which are used to compute spatial interaction. Keywords:- Spatial data, co-location patterns, segregation patterns, spatial interaction, complex patterns.

Title:	A Survey on Co-location and Segregation Patterns Discovery from Spatial Data.
Author Name:	Miss Priya S. Shejwal, Prof.Mrs. J. R. Mankar
Abstract:	ABSTRACT Spatial data mining is the extraction of spatial patterns, spatial relations and implicit knowledge, that are not explicitly stored in databases. In spatial domains, interaction between features generates two types of interaction patterns. A positive interaction (aggregation) brings a subset of features close to each other whereas a negative interaction (inhibition) results in subsets of features segregating from each other. Co-location patterns, intended to represents positive interactions, have been defined as subsets of Boolean spatial features whose instances are often seen to be located at close spatial proximity. Segregation patterns, representing negative interactions, can be defined as subsets of Boolean spatial features whose instances are infrequently seen to be located at close spatial proximity. In this paper, different approaches to discover co-location and segregation patterns like Rule based approach, event centric model approach and approaches for finding complex patterns are discussed. There are several measures which are used to compute spatial interaction. Keywords:- Spatial data, co-location patterns, segregation patterns, spatial interaction, complex patterns.
Cite this article:	Miss Priya S. Shejwal, Prof.Mrs. J. R. Mankar , " A Survey on Co-location and Segregation Patterns Discovery from Spatial Data. " , International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 3, Issue 10, October 2014 , pp. 233-237 , ISSN 2319 - 4847.
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