SPATIAL NEAREST GROUP QUERY WITH REDUNDANCY REDUCTION OPTIMIZATION

Author (s) : T.Durgadevi & C.Sindhu, Dr.R.Indhumathi & B.S.Sangeetha

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Abstract :
The  partial co-location pattern mining is an interesting and important task in spatial data mining which discovers the subsets of spatial features frequently observed together in nearby geographic space. However, the traditional framework of mining prevalent co-location patterns produces numerous redundant co-location patterns, which makes it hard for users to understand or apply. The problem of reducing redundancy in a collection of prevalent co-location patterns by utilizing the spatial distribution information of co-location instances. Theconcept of semantic distance between a co-location pattern and its super-patterns, and then define redundant co-locations. The algorithms RRclosed and RRnull to perform the redundancy reduction for prevalent co-location patterns. The former adopts the post-mining framework that is commonly used by existing redundancy reduction techniques, while the latter employs the mine-and-reduce framework that pushes redundancy reduction into the co-location mining process.

Keywords : Redundancy Reduction Optimization

Conference Date : 17/09/2021

Pages : 225

Paper ID : IESMDT223