Improved Algorithm of Complex Spatial Co-Location Pattern Based on Grid Clique

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In this paper, an algorithm is presented, which takes grid clique as a whole to find its all instance points neighbor lists in 9-neighbor area, and inserts them into the candidate extension clique list, then deletes the records that dont meet the criteria in the list, finally combines grid cliques and candidate extension clique list to generate maximal clique. The paper discusses the characteristics of spatial co-location pattern algorithm, and makes statistics of its maximal clique number generated in different distance threshold. The experiment shows that the improved algorithm can find out more maximal cliques.

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1417-1421

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October 2013

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Xu Lei. Research and Application of Spatial Association Rule Mining Technology [D], Kunming University of Science and Technology, (2011).

Google Scholar

[2] Cong Xiang-xiang. Research on Mining Spatiotemporal Co-occurrence Pattern from Large Data Sets[D], East China University of Science and Technology, (2012).

Google Scholar

[3] HU Cai-ping, QIN Xiao-lin. A Novel Positive and Negative Spatial Co-location Rules Mining Algorithm, Journal of Chinese Computer Systems. 29(2008) 80-84.

Google Scholar

[4] Ma Shuai, Li Jia. Graph Search: A New Searching Approach to the Social Computing Era, Communications of the CCF. 8(2012) 26-31.

Google Scholar

[5] Zhang Li, Sheng Yun-yao. The Research Based on the Homo-Apriori of Riddling Compression Algorithm, Computer Knowledge And Technology. 4(2008) 2038-(2041).

Google Scholar

[6] Verhein F, Al-maymat Ghazi. Fast mining of complex spatial co-location patterns using GLIMIT, Proceedings-IEEE International Conference on Data Mining, ICDM, Piscataway, NJ 08855-1331, United States: Institute of Electrical and Electronics Engineers Inc, 2007, pp.679-684.

DOI: 10.1109/icdmw.2007.49

Google Scholar

[7] Al-naymat Ghazi, Chawla Sanjay. Enumeration of maximal clique for mining spatial co-location patterns, AICCSA 08-6th IEEE/ACS International Conference on Computer Systems and Application, Piscataway, NJ 08855-1331, United States: Institute of Electrical and Electronics Engineers Computer Society, 2008, pp.126-133.

DOI: 10.1109/aiccsa.2008.4493526

Google Scholar