期刊
INFORMATION SCIENCES
卷 179, 期 19, 页码 3370-3382出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2009.05.023
关键词
Spatial data mining; Co-location patterns mining; Maximal ordered co-locations; Table instances; Order-clique-based approach
资金
- National Natural Science Foundation of China [60463004]
Most algorithms for mining spatial co-locations adopt an Apriori-like approach to generate size-k prevalence co-locations after size-(k - 1) prevalence co-locations. However, generating and storing the co-locations and table instances is costly. A novel order-clique-based approach for mining maximal co-locations is proposed in this paper. The efficiency of the approach is achieved by two techniques: (1) the spatial neighbor relationships and the size-2 prevalence co-locations are compressed into extended prefix-tree structures, which allows the order-clique-based approach to mine candidate maximal co-locations and co-location instances; and (2) the co-location instances do not need to be stored after computing some characteristics of the corresponding co-location, which significantly reduces the execution time and space required for mining maximal co-locations. The performance study shows that the new method is efficient for mining both long and short co-location patterns, and is faster than some other methods (in particular the join-based method and the join-less method). (C) 2009 Elsevier Inc. All rights reserved.
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