101. Maximal Instance Algorithm for Fast Mining of Spatial Co-Location Patterns
- Author
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Guangming Deng, Qi Li, and Guoqing Zhou
- Subjects
Computer science ,Science ,Computation ,0211 other engineering and technologies ,02 engineering and technology ,Join (topology) ,Spatial data mining ,co-location pattern mining ,Set (abstract data type) ,Data set ,Tree (data structure) ,maximal clique ,0202 electrical engineering, electronic engineering, information engineering ,spatial data mining ,General Earth and Planetary Sciences ,020201 artificial intelligence & image processing ,Fraction (mathematics) ,Spatial analysis ,Algorithm ,021101 geological & geomatics engineering ,co-location pattern - Abstract
The explosive growth of spatial data and the widespread use of spatial databases emphasize the need for spatial data mining. The subsets of features frequently located together in a geographic space are called spatial co-location patterns. It is difficult to discover co-location patterns because of the huge amount of data brought by the instances of spatial features. A large fraction of the computation time is devoted to generating row instances and candidate co-location patterns. This paper makes three main contributions for mining co-location patterns. First, the definition of maximal instances is given and a row instance (RI)-tree is constructed to find maximal instances from a spatial data set. Second, a fast method for generating all row instances and candidate co-locations is proposed and the feasibility of this method is proved. Third, a maximal instance algorithm with no join operations for mining co-location patterns is proposed. Finally, experimental evaluations using synthetic data sets and a real data set show that maximal instance algorithm is feasible and has better performance.
- Published
- 2021
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