Computer Science ›› 2016, Vol. 43 ›› Issue (2): 293-296.doi: 10.11896/j.issn.1002-137X.2016.02.061

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Co-location Patterns Mining with Time Constraint

ZENG Xin and YANG Jian   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Most of the research achievements of spatial data mining are based on the ideal spatial data and the idea of examples equality,ignoring the time constraint condition existing in the actual scene.This paper considered the existent time interval of the instance as constraint condition,redefined spatial neighborhood relation R,proposed spatial frequent pattern mining algorithm TI with time constraint,and by using time overlap as pruning condition,proposed pruning algorithm TI-C.Through empirical data analysis,under the same data set,the efficiency of TI-C algorithm is better than that of TI,the frequent pattern number of TI-C algorithm is less than that of join-based algorithm,and the frequent pattern of TI-C algorithm can accurately and truly reflect the object’s co-location relation of the actual scene.

Key words: Frequent pattern,Time overlap rate,Spatial neighborhood relation,Time constraint

[1] Han J,Kamber M.Data mining:concepts and techniques (Se-cond Edition)[M].Beijing:China Machine Press,2006:1-23
[2] Wang Li-zhen,Xie Kun-qing,Chen Tao,et al.Efficient discovery of multilevel spatial association rule using partition[J].Information & Software Technology(IST),2005,47(13):829-840
[3] Wang Li-Zhen,Zhou Li-Hua,Chen Hong-Mei,et al.The Principle and Applications of Data Warehouse and Data Mining(2nd Edition)[M].Beijing:Science Press,2009:1-19 (in Chinese) 王丽珍,周丽华,陈红梅,等.数据仓库与数据挖掘原理及应用(第2版)[M].北京:科学出版社,2009:1-19
[4] Morimoto Y.Mining frequent neighboring class sets in spatialdatabases[C]∥Proc of the Seventh ACM SIGKDD Joint Int.Conf.on Knowledge Discovery and Data Mining.New York:ACM Press,2001:353-358
[5] Huang Y,Shekhar S,Xiong H.Discovering Co-location Patterns from Spatial Data Sets:A General Approach[J].IEEE Transactions on Knowledge and Data Engineering,2004,16(12):1472-1485
[6] Huang Y,Shekhar S,Xiong H.Discovering Co-location Pat-terns from Spatial Data Sets:A General Approach[C]∥IEEE Transactions on Knowledge and Data Engineering(TKDE).2004:1472-1485
[7] Yoo J S,Shekhar S.A partial Join Approach for Mining Co-location Patterns[C]∥Proc.of the 12th Annual ACM Int.Work-shop on Geographic Information Systems.Washington DC,USA,2004:241-249
[8] Zhang X,Mamoulis N,Cheung D W,et al.Fast Mining of Spatial Co-locations[C]∥Proceedings of SIGKDD.2004:384-393
[9] Wang L,Bao Y,Lu J,et al.A New Join-less Approach for Co-location Pattern Mining[C]∥Proceedings of the IEEE 8th Int.Conf.on Computer and Information Technology (CIT2008).Syney,Australia,2008:197-202
[10] Wang L,Wu P,Chen H.Finding ProbabiListic Prevalent Co-Locations in Spatially Uncertain Data Sets[C]∥IEEE Transactions on Knowledge and Data Engineering(TKDE).2012
[11] Ouyang Zhi-ping,Wang Li-zhen,Chen Hong-mei.Mining Spatial Co-location Patterns for Fuzzy Objects[J].Chinese Journal of Computers,2011,34(10):1947-1956(in Chinese) 欧阳志平,王丽珍,陈红梅.模糊对象的空间Co-location模式挖掘研究[J].计算机学报,2011,4(10):1947-1955
[12] Lu Ye,Wang Li-zhen,Zhang Xiao-feng,et al.Spatial Co-Location Patterns Mining over Uncertain Data Based on Possible Words[J].Journal of Computer Research and Development,2010,47(Suppl.):215-221(in Chinese) 陆叶,王丽珍,陈红梅,等.基于可能世界的不确定Co-Location模式挖掘[J].计算机研究与发展,2010,47(Supp.):215-221
[13] Shekhar S,Evans M R,Kang J M,et al.Identifying patterns in Spatial information:a survey of methods[J] Wiley Interdisciplinary Reviews:Data Mining and Knowledge Discovery,2011,1(3):193-214
[14] Fan Gao-feng.Mining co-location patterns with time constraint[D].Kunming:Yunnan University,2012:19-33(in Chinese) 范高峰.带时间约束的co-location模式挖掘[D].昆明:云南大学,2012:19-33
[15] Wang Qian,Zhang Kun-peng.Im provement of MASK Algo-rithm in Privacy Preserving Data Mining[J].Journal of Chongqing University of Technology(Natural Science),2012,6(6):63-66(in Chinese) 王茜,张鲲鹏.隐私保税数据挖掘算法MASK的改进[J].重庆理工大学学报(自然科学),2012,26(6):63-66

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