|
|
- 2016
利用层次约束Delaunay三角网探测空间点事件离群模式
|
Abstract:
空间离群模式探测是空间数据挖掘的一个研究热点。以带有空间位置属性的点事件为研究对象，针对现有方法的局限性，在扩展了空间离群模式定义的基础上引入层次约束Delaunay三角网，发展了一种空间点事件离群模式探测方法（简称层次约束TIN法）。首先，借助Delaunay三角网粗略地构建空间点事件间的邻接关系；然后，利用统计学方法针对Delaunay三角网的边长特性进行三个层次约束分析，以精化空间点事件的邻近域；最后，对具有空间邻接关系的点事件集合进行统计分析，以形成一系列空间簇，并通过一个统计约束指标提取数量较少的空间簇，即空间点事件离群模式。该方法不需要人为输入参数，通过模拟数据和实际数据实验，证明该方法可以有效、稳健地识别各类空间点事件离群模式
| [1] | Gu Ping, Liu Haibo, Luo Zhiheng. Multi-clustering Based Outlier Detect Algorithm[J]. Application Research of Computers, 2013, 30(3):751-753,756(古平, 刘海波, 罗志恒. 一种基于多重聚类的离群点检测算法[J].计算机应用研究, 2013, 30(3):751-753,756) |
| [2] | Chen D C, Lu C T, Kou Y F, et al. On Detection Spatial Outliers[J]. Geoinformatica, 2008, 12:455-475 |
| [3] | Li Guangqiang, Deng Min, Zhu Jianjun, et al. Spatial Outliers Detection Considering Distances Among Their Neighbors[J]. Journal of Remote Sensing, 2009, 2:197-202(李光强, 邓敏, 朱建军,等. 一种顾及邻近域内实体间距离的空间异常检测新方法[J]. 遥感学报, 2009, 2:197-202) |
| [4] | Chawla S,Sun P. SLOM:A New Measure for Local Spatial Outliers[J]. Knowledge and Information Systems, 2006, 9(4):412-429 |
| [5] | Cai Q, He H B, Man H. Spatial Outlier Detection Based on Iterative Self-organizing Learning Model[J]. Neurocompuing, 2013, 117:161-172 |
| [6] | Jiang M F, Tseng S S, Su C M. Two-phase Clustering Process for Outliers Detection[J]. Pattern Recognition Letters, 2001, 22(6):691-700 |
| [7] | Al-Zoubi M B, Al-Dahoud A A, Yahya A. New Outlier Detection Method Based on Fuzzy Clustering[J]. WSEAS Transaction on Information Science and Applications, 2010, 7(5):681-690 |
| [8] | Zheng Mingyue. The Application of Spatial Clustering Rules in the Honghe Wetland Vegetation Form Distribution Change of Gradient[D]. Harbin:Harbin Normal University, 2013(郑明月. 空间聚类规则在洪河湿地类型分布梯度变化中的应用[D]. 哈尔滨:哈尔滨师范大学, 2013) |
| [9] | Chiu A L M, Fu A C. Enhancements on Local Outlier Detection[C]. 7th International Database Engineering and Applications Symposium, Hong Kong, China, 2003 |
| [10] | Albanese A, Pal S K, Petrosino A. Rough Sets, Kernel Set and Spatio-temporal Outlier Detection[J]. IEEE Transactions on Knowledge & Data Engineering, 2014, 26(1):194-207 |
| [11] | Li Deren, Wang Shuliang, Li Deyi, et al. Theories and Technologies of Spatial Data Mining and Knowledge Discovery[J]. Geomatics and Information Science of Wuhan University, 2002, 27(3):221-233(李德仁, 王树良, 李德毅,等. 论空间数据挖掘和知识发现的理论和方法[J]. 武汉大学学报·信息科学版, 2002, 27(3):221-233) |
| [12] | Jin W, Tung A K H, Han J W, et al. Ranking Outliers Using Symmetric Neighborhood Relationship[C].10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, Singapore, 2006 |
| [13] | Han J, Kamber M, Pei J. Data Mining:Concepts and Techniques[M]. 3rd Edition. San Francisco:Morgan Kaufman, 2012 |
| [14] | Hawkins D M. Identification of Outliers[M]. London:Chapman and Hall, 1980 |
| [15] | Shekhar S, Lu C T, Zhang P S. A Unified Approach to Detecting Spatial Outliers[J]. GeoInformatica, 2003, 7(2):139-166 |
| [16] | Ramaswamy S, Rastogi R, Shim K. Efficient Algorithms for Mining Outliers from Large Data Sets[C]. 2000 ACM SIGMOD International Conference on Management of Data, Dallas, USA, 2000 |
| [17] | Breunig M M, Kriegel H P, Ng R T, et al. LOF:Identifying Density-based Local Outliers[C].2000 ACM SIGMOD International Conference on Management of Data, Dallas, USA, 2000 |
| [18] | Chen F, Lu C T, Boedihardjo A P. GLS-SOD:A Generalized Local Statistical Approach for Spatial Outlier Detection[C].16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington DC, USA, 2010 |
| [19] | Deng M, Liu Q L, Cheng T, et al. An Adaptive Spatial Clustering Algorithm Based on Delaunay Triangulation[J]. Computer, Environment, Urban and Systems, 2011, 35(4):320-332 |
| [20] | Ma Ronghua, He Zengyou. Fast Mining of Spatial Outliers from GIS Database[J]. Geomatics and Information Science of Wuhan University, 2006, 31(8):679-682(马荣华, 何增友. 从GIS数据库中挖掘空间离群点的一种高效算法[J]. 武汉大学学报·信息科学版, 2006, 31(8):679-682) |
| [21] | Liu Qiliang, Li Guangqiang, Deng Min. A Local Distribution Based Spatial Clustering Algorithm[J]. Geomatics and Information Science of Wuhan University, 2010, 35(3):373-377(刘启亮, 李光强, 邓敏. 一种基于局部分布的空间聚类算法[J]. 武汉大学学报·信息科学版, 2010, 35(3):373-377) |
| [22] | Telang A, Deepak P, Joshi S, et al. Detecting Localized Homogeneous Anomalies over Spatio-temporal Data[J]. Data Mining and Knowledge Discovery, 2014, 28(5-6):1480-1502 |
| [23] | Shi Yan, Liu Qiliang, Deng Min, et al. A Hybrid Spatial Clustering Method Based on Graph Theory and Spatial Density[J]. Geomatics and Information Science of Wuhan University, 2012, 37(11):1276-1280(石岩, 刘启亮, 邓敏,等. 融合图论与密度思想的混合空间聚类方法[J]. 武汉大学学报·信息科学版, 2012, 37(11):1276-1280) |
| [24] | Hayes M. Drought Indices[OL]. http://www.drought.unl.edu/whatis/indices.htm,2013 |
