空间并置模式挖掘研究
Research on Spatial Co-Locations Mining: A Survey
DOI: 10.12677/CSA.2018.83038, PDF,    科研立项经费支持
作者: 吴萍萍*, 王丽珍, 邓世昆, 刘玉娥：云南大学滇池学院理工学院计算机科学与工程系，云南，昆明
关键词: 空间模式挖掘；并置模式；算法；Spatial Pattern Mining； Co-Location Pattern； Algorithm
摘要: 由于移动电话、GPS、传感器和其他的无线设备的广泛使用，空间数据迅速增长。空间数据独特的复杂性以及其在现实中的广泛应用，使得空间数据挖掘成为一个很有前途的发展方向。而作为空间数据挖掘领域的重要任务之一，空间并置模式挖掘也受到越来越多的关注。空间并置模式挖掘旨在寻找其实例在邻近域中频繁地并置出现的空间特征。本文从挖掘的并置模式类型、挖掘方法和应用三个方面简要介绍空间并置模式挖掘的研究现状，并总结了研究中一些有趣的挑战。
Abstract: Due to the widespread use of mobile phones, GPS, sensors and other wireless devices, spatial data set is rapidly growing. The unique complexity and widely application in the real world make spatial data mining a promising field. As one of the important researches in spatial data mining, the spatial co-location pattern mining attracts more and more attention. Spatial co-location pattern mining aims to find the spatial features whose instances frequently co-located in neighborhood. We briefly introduce the current research of spatial co-location pattern mining from three aspects: type of co-location pattern, method of mining and application. At last, we conclude some interesting challenges in the field of co-location pattern mining.
文章引用：吴萍萍, 王丽珍, 邓世昆, 刘玉娥. 空间并置模式挖掘研究[J]. 计算机科学与应用, 2018, 8(3): 328-338. https://doi.org/10.12677/CSA.2018.83038

参考文献

[1] 王丽珍, 陈红梅. 空间模式挖掘理论与方法[M]. 北京: 科学出版社, 2014.
[2] Morimoto, Y. (2001) Mining Frequent Neigh-boring Class Sets in Spatial Databases. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 26-29 August 2001, San Francisco, 353-358. [Google Scholar] [CrossRef] 
[3] Huang, Y., Shekhar, S. and Xiong, H. (2004) Discovering Co-Location Patterns from Spatial Data Sets: A General Approach. IEEE Transactions on Knowledge & Data Engineering, 16, 1472-1485. [Google Scholar] [CrossRef] 
[4] Yao, X., Chen, L., Wen, C., et al. (2018) A Spatial Co-Location Mining Algorithm That Includes Adaptive Proximity Improvements and Distant Instance References. International Journal of Geographical Information Science, 3, 1-26. [Google Scholar] [CrossRef] 
[5] Jin, S.Y., Boulware, D. and Kimmey, D. (2014) A Parallel Spatial Co-Location Mining Algorithm Based on MapReduce. IEEE International Congress on Big Data, 2014, 25-31.
[6] 吴萍萍, 王丽珍, 周永恒. 带模糊属性的空间Co-Location模式挖掘研究[J]. 计算机科学与探索, 2013, 7(4): 348-358.
[7] 江万国, 王丽珍, 方圆, 等. 领域驱动的高效用co-location模式挖掘方法[J]. 计算机应用, 2017, 37(2): 322-328.
[8] Dai, B.R. and Lin, M.Y. (2011) Efficiently Mining Dynamic Zonal Co-Location Patterns Based on Maximal Co-Locations. IEEE International Conference on Data Mining Workshops, 11-11 December 2011, Vancouver, BC, 861-868. [Google Scholar] [CrossRef] 
[9] Zhao, J., Wang, L., Bao, X., et al. (2016) Mining Co-Location Patterns with Spatial Distribution Characteristics. IEEE International Conference on Computer, Information and Telecommunication Systems, 6-8 July 2016, Kunming, 1-5. [Google Scholar] [CrossRef] 
[10] Qian, F., Chiew, K., He, Q., et al. (2013) Discovery of Regional Co-Location Patterns with k-Nearest Neighbor Graph. Advances in Knowledge Discovery and Data Mining, 2013, 174-186. [Google Scholar] [CrossRef] 
[11] Barua, S. (2014) Mining Statistically Sound Co-Location Patterns at Multiple Distances. International Conference on Scientific & Statistical Database Management, Aalborg, 30 June 2014, 1-12.
[12] Wang, L., Zhou, L., Lu, J., et al. (2009) An Order-Clique-Based Approach for Mining Maximal Co-Locations. Information Sciences, 179, 3370-3382. [Google Scholar] [CrossRef] 
[13] Jin, S.Y. and Bow, M. (2011) Mining Maximal Co-Located Event Sets. Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, Shenzhen, 24-27 May 2011, 351-362.
[14] 胡新, 王丽珍, 周丽华, 等. 空间极大co-location模式挖掘研究[J]. 计算机科学与探索, 2014, 8(2): 150-160.
[15] Jin, S.Y. and Bow, M. (2011) Mining Top-k Closed Co-Location Patterns. IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, Fuzhou, 29 June-1 July 2011, 100-105.
[16] Wang, L., Bao, X., Chen, H., et al. (2018) Effective Lossless Con-densed Representation and Discovery of Spatial Co-Location Patterns. Information Sciences, 436, 197-213. [Google Scholar] [CrossRef] 
[17] Liu, B., Chen, L., Liu, C., et al. (2015) RCP Mining: Towards the Summarization of Spatial Co-Location Patterns. In: Advances in Spatial and Temporal Databases, Springer International Publishing, Berlin, 451-469.
[18] Wang, L., Bao, X. and Zhou, L. (2018) Redundancy Reduction for Prevalent Co-Location Patterns. IEEE Transactions on Knowledge & Data Engineering, 30, 142-155. [Google Scholar] [CrossRef] 
[19] Wang, L., Han, J., Chen, H., et al. (2016) Top-k Probabilistic Prevalent Co-Location Mining in Spatially Uncertain Data Sets. Frontiers of Computer Science, 10, 1-16. [Google Scholar] [CrossRef] 
[20] 姚华传, 王丽珍, 陈红梅, 等. 面向海量数据的空间co-location模式挖掘新算法[J]. 计算机科学与探索, 2015, 9(1): 24-35.
[21] Wang, L., Wu, P. and Chen, H. (2013) Finding Probabilistic Prevalent Co-Locations in Spatially Uncertain Data Sets. IEEE Transactions on Knowledge & Data Engineering, 25, 790-804. [Google Scholar] [CrossRef] 
[22] Huang, Y., Pei, J. and Xiong, H. (2006) Mining Co-Location Patterns with Rare Events from Spatial Data Sets. Geoinformatica, 10, 239-260. [Google Scholar] [CrossRef] 
[23] 冯岭, 王丽珍, 高世健. 一种带稀有特征的空间co-location模式挖掘新方法[J]. 南京大学学报(自然科学), 2012, 48(1): 99-107.
[24] Deng, M., Cai, J., Liu, Q., et al. (2017) Multi-Level Method for Discovery of Regional Co-Location Patterns. International Journal of Geograph-ical Information Science, 31, 1846-1870. [Google Scholar] [CrossRef] 
[25] Jiang, Y., Wang, L., Lu, Y., et al. (2010) Discovering Both Positive and Negative Co-Location Rules from Spatial Data Sets. International Conference on Software En-gineering and Data Mining, Chengdu, 23-25 June 2010, 398-403.
[26] 吴凤萍. 正负co-location规则挖掘算法研究[D]: [硕士学位论文]. 昆明: 云南大学, 2008.
[27] 胡彩平, 秦小麟. 一种新的正负空间同位规则挖掘算法[J]. 小型微型计算机系统, 2008, 29(1): 80-84.
[28] 赵楠. 基于正负空间co-location模式挖掘的城市规划分析[D]: [硕士学位论文]. 昆明: 云南大学, 2012.
[29] Sheng, C., Hsu, W. and Lee, M. (2008) Discovering Spatial Interaction Patterns. Springer, Berlin, 95-109. [Google Scholar] [CrossRef] 
[30] 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, 2012(10): 1947-1956.
[31] Ouyang, Z., Wang, L. and Wu, P. (2017) Spatial Co-Location Pattern Discovery from Fuzzy Objects. International Journal on Artificial Intelligence Tools, 26, Article ID: 1750003. [Google Scholar] [CrossRef] 
[32] Wang, L., Chen, H., Zhao, L., et al. (2010) Efficiently Mining Co-Location Rules on Interval Data. International Conference on Advanced Data Mining and Applications, Chongqing, 19-21 November 2010, 477-488. [Google Scholar] [CrossRef] 
[33] Wang, L., Guan, P., Chen, H., et al. (2013) Mining Co-Locations from Spa-tially Uncertain Data with Probability Intervals. In: WAIM 2013 Workshops, Springer-Verlag, Berlin, LNCS 7901, 301-314.
[34] 陆叶, 王丽珍, 张晓峰. 从不确定数据集中挖掘频繁Co-Location模式[J]. 计算机科学与探索, 2009, 3(6): 656-664.
[35] 陆叶, 王丽珍, 陈红梅, 等. 基于可能世界的不确定空间co-location模式挖掘研究[J]. 计算机研究与发展, 2010, 47(z1.): 215-221.
[36] Huang, Y., Zhang, L. and Yu, P. (2005) Can We Apply Projection Based Frequent Pattern Mining Paradigm to Spatial Co-Location Mining? Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, Hanoi, 18-20 May 2005, 719-725.
[37] Wang, L., Bao, Y., Lu, J., et al. (2008) A New Join-Less Approach for Co-Location Pattern Mining. International Conference on Computer and Information Technology, Sydney, 8-11 July 2008, 197-202.
[38] Yue, H., Zhu, X., Ye, X., et al. (2017) The Local Colocation Patterns of Crime and Land-Use Features in Wuhan, China. International Journal of Geo-Information, 6, 307. [Google Scholar] [CrossRef] 
[39] 包旭光, 王丽珍, 陈红梅. 基于co-location模式和本体的地址选择算法[J]. 计算机工程与应用, 2017, 53(24): 15-22.
[40] Wang, L., Bao, Y., Lu, J., et al. (2009) A Web-Based Visual Spatial Co-Location Patterns’ Mining Prototype System. International Conference on Cyberworlds, Bradford, 7-11 September 2009, 675-681.
[41] 田晶, 王一恒, 颜芬, 等. 一种网络空间现象同位模式挖掘的新方法[J]. 武汉大学学报(信息科学版), 2015, 40(5): 652-660.
[42] Weiler, M., Schmid, K.A., Mamoulis, N., et al. (2015) Geo-Social Co-Location Mining. In: International ACM Workshop on Managing and Min-ing Enriched Geo-Spatial Data, ACM, New York, 19-24.
[43] Wang, L., Wu, P., Fan, G., et al. (2013) Extracting Prevalent Co-Location Patterns from Historic Spatial Data. In: International Conference on Web-Age Information Management, Springer, Berlin Heidelberg, 287-300.
[44] Lu, J., Wang, L., Fang, Y., et al. (2018) Mining Strong Symbiotic Patterns Hidden in Spatial Prevalent Co-Location Patterns. Knowledge-Based Systems, 146, 190-202. [Google Scholar] [CrossRef] 
[45] Wang, L., Jiang, W., Chen, H., et al. (2017) Efficiently Mining High Utility Co-Location Patterns from Spatial Data Sets with Instance-Specific Utilities. In: International Conference on Database Systems for Advanced Applications, Springer, Cham, 458-474.