A New Join-less Approach for Co-location Pattern Mining

Lizhen Wang, Yuzhen Bao, Joan Lu, Jim Yip

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

87 Citations (Scopus)


With the rapid growth and extensive applications of the spatial dataset, it's getting more important to solve how to find spatial knowledge automatically from spatial dataseis. Spatial co-location patterns represent the subsets of features whose instances are frequently located together in geographic space. It's difficult to discovery 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 the table instances of colocation patterns. The essence of co-location patterns discovery and three kinds of co-location patterns mining algorithms proposed in recent years are analyzed, and a new join-less approach for co-location patterns mining, which based on a data structure--CPI-tree (Co-location Pattern Instance Tree), is proposed. The CPI-tree materializes spatial neighbor relationships. All co-location table instances can be generated quickly with a CPI-tree. This paper proves the correctness and completeness of the new approach. Finally, an experimental evaluation using synthetic dataseis and a real world dataset shows that the algorithm is computationally more efficient than the join-less algorithm.

Original languageEnglish
Title of host publicationProceedings - 2008 8th IEEE International Conference on Computer and Information Technology, CIT 2008
EditorsQiang Xu, Xiangjian He, Quang Vinh Nguyen, Wenjing Jia, Maolin Huang
Number of pages6
ISBN (Print)9781424423576
Publication statusPublished - 8 Aug 2008
EventIEEE 8th International Conference on Computer and Information Technology - Sydney, Australia
Duration: 8 Jul 200811 Jul 2008
Conference number: 8


ConferenceIEEE 8th International Conference on Computer and Information Technology
Abbreviated titleCIT2008
Internet address


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