面向空间数据复杂性特征的聚类分析方法研究
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摘要
随着数据获取技术的快速发展，数据呈现出多样化、海量化的发展趋势。针对空间信息的空间数据被大量收集，成为探索空间领域知识的根本途径和重要依据，迫切需要面向空间数据的空间数据挖掘技术来发现其中隐含的有用知识。作为空间数据挖掘的重要分支，空间聚类以其较强的实用性和高效性，成为相关领域的研究热点。
本文在深入研究空间数据挖掘、空间聚类知识和现有方法的基础上，面向空间数据体现出的复杂性特征中的海量特征、高维特征、带障碍约束特征以及多尺度特征，研究高效的空间数据聚类分析方法。
针对空间数据的海量特征，运用善于进行大规模数据聚类分析的经典K-Means算法进行聚类分析，对于其存在的k值预设及初始聚类中心随机选取导致算法准确率、效率不高的问题，利用空间数据的特点，提出一种基于最优划分的K-Means初始聚类中心选取方法，提高了海量空间数据的条件下K-Means算法进行聚类分析的运算效率和算法准确率；
针对空间数据的高维特征，提出一种高效的基于模糊扩展的高维空间数据子空间聚类算法，该算法基于模糊集对确定聚类边界起决定作用的稀疏单元网格进行模糊扩展，考虑相邻网格内样本点的相关性，改善了高维数据子空间聚类不平滑、边界不明确、易产生无意义过度聚类的问题，高效进行高维空间数据的聚类分析；
针对空间数据多障碍约束的特征，提出一种基于网格的带障碍约束空间数据分级聚类算法，算法继承了基于网格聚类算法的优点，能够处理任意形状的障碍物并产生任意形状的簇，同时，算法采用分级策略对考察数据集进行障碍条件下聚类分析，降低了计算样本点间障碍距离产生的算法时空复杂度，提高了障碍约束条件下空间数据聚类分析的效率；
针对空间数据的多尺度特征，提出一种基于等密度线的空间数据多尺度聚类分析方法，该多尺度空间聚类算法借鉴等高线思想，利用考察样本集等密度线的天然尺度性进行尺度条件下的空间数据聚类分析。
本论文面向空间数据的海量特征、高维特征、带障碍约束特征以及多尺度特征分别进行了空间聚类分析方法的研究，并通过仿真实验分别证明了方法的可行性、有效性和高效性。
With the development of data capture technologies, the diversification andmagnanimity development trend is shown. Huge amounts of spatial data faced onspatial information are collected. Spatial data mining techniques are neededurgently to find hidden useful knowledge. Spatial Data Mining which is animportant branch of spatial data mining for its strong cluster of practicality andefficiency, and research into related areas becomes a hot spot.
Based on the in-depth research of spatial data mining, spatial clustering andexisting methods, the high efficient spatial data clustering analysis methodsoriented to spatial data complexity of the characteristic of the massivecharacteristic, high-dimensional characteristic, data with obstacles and constraintscharacteristic and multi-scale characteristic are researched in this thesis.
According to the massive characteristic of spatial data, the classical K-Meansalgorithm which is good at clustering massive data set is used. An algorithm forinitialization of K-Means clustering center based on optimized division used thecharacter of spatial data is proposed. This method solved the existence problem ofpre-k value, cluster centers selecting randomly in classical K-Means algorithm.The efficiency and accuracy of clustering algorithm oriented to massive spatialdata is improved.
According to the high-dimensional characteristic of spatial data, a fuzzyextended based algorithm of high dimensional spatial clustering is proposed. Thespare grids which are more important to estimate the edges of clusters areextended based on fuzzy sets to consider the correlativity of spatial data inadjacent grids in this algorithm. It aims at ameliorating problems ofun-smoothness clustering, unclear boundary of spatial clustering results, andexcessive meaningless small clustering of existence high-dimensional subspacespatial clustering algorithm, and clusters with high-dimensional spatial data moreefficiently.
According to the spatial data with obstacles and constrains characteristic, a grid-based hierarchical spatial clustering algorithm is proposed. The advantage ofgrid-based clustering algorithm is inherited. The algorithm processes arbitraryshape obstacle and finds arbitrary shape clusters efficiently. Meanwhile, thehierarchical strategy is used to reduce the complexity of clustering in presence ofobstacles and constraints and the operation efficiency of algorithm is improved.
According to the multi-scale characteristic of spatial data, a spatialmulti-scale clustering algorithm based on density-isoline is proposed. Thought ofcontour line is referenced in the algorithm, and the spatial clustering analysisresults under multi-scale condition are found efficiently by using of naturaldensity standard of density-isoline in this spatial multi-scale clustering algorithm.
The methods which orient to spatial data characteristics of massivecharacteristic, high-dimensional characteristic, data with obstacles and constraintscharacteristic and multi-scale characteristic are researched. The feasibility, validity,and efficiency of each new method is demonstrated by simulate experiment.
本文在深入研究空间数据挖掘、空间聚类知识和现有方法的基础上，面向空间数据体现出的复杂性特征中的海量特征、高维特征、带障碍约束特征以及多尺度特征，研究高效的空间数据聚类分析方法。
针对空间数据的海量特征，运用善于进行大规模数据聚类分析的经典K-Means算法进行聚类分析，对于其存在的k值预设及初始聚类中心随机选取导致算法准确率、效率不高的问题，利用空间数据的特点，提出一种基于最优划分的K-Means初始聚类中心选取方法，提高了海量空间数据的条件下K-Means算法进行聚类分析的运算效率和算法准确率；
针对空间数据的高维特征，提出一种高效的基于模糊扩展的高维空间数据子空间聚类算法，该算法基于模糊集对确定聚类边界起决定作用的稀疏单元网格进行模糊扩展，考虑相邻网格内样本点的相关性，改善了高维数据子空间聚类不平滑、边界不明确、易产生无意义过度聚类的问题，高效进行高维空间数据的聚类分析；
针对空间数据多障碍约束的特征，提出一种基于网格的带障碍约束空间数据分级聚类算法，算法继承了基于网格聚类算法的优点，能够处理任意形状的障碍物并产生任意形状的簇，同时，算法采用分级策略对考察数据集进行障碍条件下聚类分析，降低了计算样本点间障碍距离产生的算法时空复杂度，提高了障碍约束条件下空间数据聚类分析的效率；
针对空间数据的多尺度特征，提出一种基于等密度线的空间数据多尺度聚类分析方法，该多尺度空间聚类算法借鉴等高线思想，利用考察样本集等密度线的天然尺度性进行尺度条件下的空间数据聚类分析。
本论文面向空间数据的海量特征、高维特征、带障碍约束特征以及多尺度特征分别进行了空间聚类分析方法的研究，并通过仿真实验分别证明了方法的可行性、有效性和高效性。
With the development of data capture technologies, the diversification andmagnanimity development trend is shown. Huge amounts of spatial data faced onspatial information are collected. Spatial data mining techniques are neededurgently to find hidden useful knowledge. Spatial Data Mining which is animportant branch of spatial data mining for its strong cluster of practicality andefficiency, and research into related areas becomes a hot spot.
Based on the in-depth research of spatial data mining, spatial clustering andexisting methods, the high efficient spatial data clustering analysis methodsoriented to spatial data complexity of the characteristic of the massivecharacteristic, high-dimensional characteristic, data with obstacles and constraintscharacteristic and multi-scale characteristic are researched in this thesis.
According to the massive characteristic of spatial data, the classical K-Meansalgorithm which is good at clustering massive data set is used. An algorithm forinitialization of K-Means clustering center based on optimized division used thecharacter of spatial data is proposed. This method solved the existence problem ofpre-k value, cluster centers selecting randomly in classical K-Means algorithm.The efficiency and accuracy of clustering algorithm oriented to massive spatialdata is improved.
According to the high-dimensional characteristic of spatial data, a fuzzyextended based algorithm of high dimensional spatial clustering is proposed. Thespare grids which are more important to estimate the edges of clusters areextended based on fuzzy sets to consider the correlativity of spatial data inadjacent grids in this algorithm. It aims at ameliorating problems ofun-smoothness clustering, unclear boundary of spatial clustering results, andexcessive meaningless small clustering of existence high-dimensional subspacespatial clustering algorithm, and clusters with high-dimensional spatial data moreefficiently.
According to the spatial data with obstacles and constrains characteristic, a grid-based hierarchical spatial clustering algorithm is proposed. The advantage ofgrid-based clustering algorithm is inherited. The algorithm processes arbitraryshape obstacle and finds arbitrary shape clusters efficiently. Meanwhile, thehierarchical strategy is used to reduce the complexity of clustering in presence ofobstacles and constraints and the operation efficiency of algorithm is improved.
According to the multi-scale characteristic of spatial data, a spatialmulti-scale clustering algorithm based on density-isoline is proposed. Thought ofcontour line is referenced in the algorithm, and the spatial clustering analysisresults under multi-scale condition are found efficiently by using of naturaldensity standard of density-isoline in this spatial multi-scale clustering algorithm.
The methods which orient to spatial data characteristics of massivecharacteristic, high-dimensional characteristic, data with obstacles and constraintscharacteristic and multi-scale characteristic are researched. The feasibility, validity,and efficiency of each new method is demonstrated by simulate experiment.
引文
[1] J. Han, M. Kamber. Data Mining: Concepts and Techniques [M], San Francisco: Academic Press, 2001
[2] Krzysztof, Kpperski, Junas Adhikary, Jiawei Han. Mining: Progress Workshop on and Challenges Survey Research Issues on paper[R]. Data Spatial Data SIGMOD'06 Mining and Knowledge (DMKD'06), Canada:Montreal, 2006
[3] 毛克彪，覃志豪，李海涛，周若鸿．基于空间数据仓库的空间数据挖掘研究．遥感信息(理论研究)．2002：19-26页
[4] 张瑞菊，陶华学．GIS与空间数据挖掘集成问题的研究．勘察科学技术．2003，2(1)：21-24页
[5] 毛克彪，田庆久．空间数据挖掘技术方法及应用．遥感技术与应用，2002，17(4)：198-204页
[6] K.L. Kaufman, P.J. Rousseeuw. Finding Groups in Data: An introduction to Cluster Analysis, John Wiley & Sons. 1990.
[7] R. Ng, J, Han. Efficient and Effective Clustering Method for Spatial Data Mining. Proc.1994 Int. Conf. Very Large Data Bases (VLDB'94), 1994:144-155P
[8] T. Zhang, R. Ramakrishnan, M. Livny. Birch: An Efficient Data Clustering Method for Very Large Databases. Proc. 1996 ACM-SIGMOD Inc. Conf. Management of Data (SIGMOD'96), 1996: 103-114P
[9] S.Guha, R.Rastogi, K. Shim. CURE: An Efficient Clustering Algorithm for Large Databases. In Proc. of the ACM SIGMOD, 1998.
[10] G. Karypis, E.H. Han, V. Kumar. CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling. COMPUTER. 1999,2(32): 68-75P
[11] M. Ester, H. P. Kriegel, and X. Xu, A density-based algorithm for discovering clusters in large spatial databases. Knowledge Discovery and Data Mining, Portland, 1996: 226-23 IP
[12] A. Hinneburg and D. A. Keim, An efficient approach to clustering in large multimedia databases with noise. Knowledge Discovery and Data Mining, New York, 1998:58-65P
[13] M. nkerst, M. breunig, H. P. Kriegel, and J. Sander, Ordering points to identify the clustering structure. Management of Data, Philadelphia, 1999: 49-60P
[14] J. Wang, R. Yang, Muntz. STING: A Statistical Information Grid Approach to Spatial Data Mining. Proc. 1997 Int. Conf. Very Large Data Bases (VLDB'97). 1997: 186-195P
[15] X.Xu, M.Ester, H. P. Kriegel, et al. A Distribution-Based Clustering Algorithm for Mining in Large Spatial Databases. Proc. 1998 Int. Conf. Data Engineering. Orlando: IEEE Computer Society Presss. 1998: 324-331P
[16] R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, Automatic subspace clustering of high dimensional data for data mining applications. Management of Data, Seattle, 1998: 94-105P
[17] G. Sheikholeslami, S. Chatterjee, and A. Zhang, A multi-resolution clustering approach for very large spatial databases. Very Large Data Bases, New York, 1998: 428-439P
[18] Haofeng Zhou, Qingqing Yuan, Zunping Cheng, Baile Shi. PHC: A Fast Partition and Hierarchy based Clustering Algorithm. Journal of Computer Science and Technology. 2003, 18(3): 407-411P
[19] Ding Zhou, Zunping Cheng, Chen Wang, Haofeng Zhou, Wei Wang, Baile Shi. SUDEPHIC: Self-tuning Density based Partitioning and Hierarchical Clustering. In proceedings of the 9th International Conference on Database Systems for Advanced Applications (DASFAA'04). 2004
[20] Yu Qian, Kang Zhang. GraphZip: A Fast and Automatic Compression Method for Spatial Data Clustering. 2004 ACM Symposium on Applied Computing, 2004
[21] Fabrizio Angiulli, Clara Pizzuti, Massimo Ruffolo. DESCRY: A Grid and Density based Clustering Algorithm for Very Large Data Sets. In proceedings of the 5th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL'04). 2004: 8:25-27P
[22] J. Vitter. Random sampling with a reservoir. CM Transaction on Mathematical Software. 1995,11(1): 37-57P
[23] J.E.Jackson. A User's Guide To Principal Components. John Wiley&Sons, 1991.
[24] A. K. Jain, R. C. Dubes. Algorithms for Clustering Data. Prentice Hall, 1998.
[25] T. Kohonen. Self-Organization and Associated Memory. Springer-Verlag, 1998.
[26] G. Sheikholeslani, S.Chatterjee, A. Zhang. WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases. In Proceedings of the 24th VLDB conference. 1998.
[27] Chen Ning, Chen An, Zhou Long-xiang. An Effective Clustering Algorithm in Large Tansaction Databases. Softwares Journal. 2001, 12(4): 475-484P
[28] Joliffe, I. Principal Component Analysis. Springer-Verlag. NewYork, NY, 1996.
[29] M. Wberry, S.T. dumais, G. W. O'Brien. Using linear algebra for intelligent information retrieval. SIAM Revies. 1995, 37:573-595.
[30] Keogh E., Chakrabarti K., Pazzani M., Mehrotra, S.. Dimensionality reduction for fast similarity search in large time series database. Journal of Knowledge and Information System. 2001.
[31] Cheng, C., Fu, A., Zhang, Y. Entropy-based subspace clustering for mining numerical data. Proceedings in ACM SIGKDD. San Diego, CA .1999: 84-93P
[32] Goil, S., Nagesh, H., Choudhary, A. MAFIA: Efficient and scalable subspace clustering for very large data sets. Technical report CPDC-TR-9906-010. Northwestern University. 1999
[33] 刘勘，周晓峥，周洞汝．一种基于排序子空间的高维聚类算法及其可视化研究．计算机研究与发展．2003，40(10)：1509-1513页
[34] 王生生，刘大有，曹斌，刘杰．一种高维空间数据的子空间聚类算法．计算机应用．2005，11(25)：2615-2617页
[35] A. H. Pilevar, M. Sukumar. GCHL: A grid-clustering algorithm for high-dimensional very large spatial data bases. Pattern Recognition Letters.2005, 11(26): 999-1010P
[36] A.K.H. Tung, J. Hou, J. Han. Spatial Clustering in the Presence of Obstacles. Int. Conf. on Data Engineering. Heidelberg, Germany. 2001:359-367P
[37] V. Estivill Castro, I. J. Lee. AutoClust+: Automatic Clustering of Point-Data Sets in the Presence of Obstacles. Int. Workshop on Temporal,Spatial and Spatio-Temporal Data Mining. Lyon, France. 2000: 133-146P
[38] V. Estivill Castro, I. J. Lee. AutoClust: Automatic Clustering via Boundary Extraction for Massive Point-Data Sets. Int. Conf. On Geo-computation'00.2000: 23-25P
[39] O.R. Zaiane, C. H. Lee. Clustering Spatial Data When Facing Physical Constraints. Int. the IEEE International Conf. On Data Mining. Maebashi City, Japan. 2002: 737-740P
[40] X. Wang, H. J. Hamilton. Density-Based Spatial Clustering in the Presence of Obstacles and Facilitators. Int. the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases. Pisa, Italy.2004: 446-458P
[41] X. Wang, H. J. Hamilton. DBRS: A Density-Based Spatial Clustering Method with Random Sampling. Proc. the 7th International Conference on PAKDD. Seoul, Korea. 2003: 563-575P
[42] Hai-Dong Yang, Fei-qi Deng. The Study on Immune Spatial Clustering Model based on Obstacle. Int. the 3th International Conference on Machine Learning and Cybernetics. Shanghai, China. 2004: 378-382P
[43] Leung Y. Zhang J. Xu Z. Clustering by scale-space filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22:1396-1410P
[44] 汪闽．空间数据挖掘方法研究．中国科学院地理科学与资源研究所博士论文，2003
[45] 邸凯昌，李德仁，李德毅．从空间数据库发现聚类：一种基于数学形态学的算法．中国图像图形学报．1998，3(3)：173-178页
[46] 陈江平，李平湘．点对象的多尺度空间关联规则挖掘算法研究．计算机应用．2004，24(7)：18-21页
[47] 张文君，顾行发，陈良富等．基于均值-标准差的k均值初始聚类中心选取算法．遥感学报．2006，10(5)：715-721页
[48] K. Beyer, J. Goldstein, R. Ramakhrisnan, et al. Nearest neighbor' meaningful. Proc. the 7th Int. Conf. Database Theory (ICDT'99).http://citeseer.ist.psu.edu/605885.html.1999
[49] A. Hinneburg, C. C. Aggarwal, D. A. Keim. What is the nearest neighbor in high dimensional spaces. Proc. the 26th Int. Conf. Very Large Data Bases. San Francisco. 2000
[50] 汪闽，周成虎，裴韬，骆剑承．MSCMO：基于数学形态学算子的尺度空间聚类算法．遥感学报．2004，8(1)：45-50页
[51] 沙占江，马海州，李玲琴等．多尺度空间分层聚类算法在土地利用与土地覆被研究中的应用．地理科学．2004，24(4)：477-483页
[52] 李小斌，田铮．基于谱聚类的图像多尺度随机树分割．中国科学E辑：信息科学．2007，37(8)：1073-1085P
[53] Takuya Tsuji, Akihito Ito, Toshitsugu Tanaka. Multi-scale of clustering particles. Powder Technology. 2007, 179(8): 115-125P
[54] D.Y. Tzou, J.K. Chen, R. Roybal, J.E. Beraun. Cluster potentials for multi-scale interactions. International Journal of Heat and Mass Transfer.2004, 47: 2949-2959P
[55] Yan Jin. Ling Wang, Yoohwan Kim, Xiaozong Yang. EEMC: An energy-efficient multi-level clustering algorithm for large-scale wireless sensor networks. Computer Networks 2007, 10(5): 1-21P
[56] 张云涛，龚玲．数据挖掘原理与技术．北京：电子工业出版社，2004：35-41页
[57] Eliseo Clementini, P D Felice. Mining multiple-level spatial association rules for objects with a broad boundary. Data & Knowledge Engineering.2000, 34(2): 251-270P
[58] 刘大有，王生生，虞强源，胡鹤．基于定性空间推理的多层空间关联规则挖掘算法．计算机研究与发展，2004，41(4)：565-570页
[59] 袁红春，熊范纶．元规则指导下的逐步求精多层空间关联规则挖掘算法．计算机工程，2004，30(8)：33-36，39页
[60] Vladimir, Estivill-Castro, Ickjai Lee. Spatial Clustering with obstacles for Geo-graphical Data Mining. ISPRS Journal of Photogrammetry & Remote Sensing. 2004, 59(5): 21-34P
[61] 孙建国，赵军，闫浩文．一个空间数据挖掘原型系统的设计与实现．兰州交通大学学报，2004，23(6)：74-79页
[62] 邹力琨，王丽珍，何倩等．空间数据挖掘发展研究．计算机工程与应用，2003，11(2)：186-188页
[63] 袁红春，熊范纶，杭小树等．一个适用于地理信息系统的数据挖掘工具-GIS Miner．中国科学技术大学学报，2002，32(4)：217-224页
[64] A.K. Jain, R. C. Dubes. Algorithms for Clustering Data. Prentice Hall,1998
[65] Qian Wei-ning, Zhou Ao-ying. Analyzing Popular Clustering Algorithms from diffrernt Viewpoints. Journal of software, 2002, 13(8): 148-151P
[66] Martin Ester, Alexander Frommelt, Hans Peter kriegel. Spatial Data Mining: Database Primitives. Algorithms and Efficient DBMS Support Data Mining and Knowledge Discovery. 2000, 10(4): 193-216P
[67] 杨春成．空间数据挖掘中聚类分析算法的研究．解放军信息工程大学．博士学位论文．2004：1-4页．
[68] 汪闽．空间数据挖掘方法研究．中国科学院地理科学与资源研究所博士论文，2003．
[69] Yuhang Wang, Pillia Makedon, Amit Chakrabarti, R O-Histogram:Efficient representation of spatial relations between objects of arbitrary topology. Proceedings of the 12th annual ACM international conference on Multimedia. USA, 2004:10-16P
[70] 赵军喜，孙庆辉，张毅．GIS中几何对象之间的空间关系．测绘学院学报．2002，19(4)：306-309页
[71] 郭薇，陈军．三位拓扑空间关系的形式化描述及拓扑空间关系最小集．武汉水利电力大学学报．1996，29(4)：85-90页
[72] Goyal R.K..Similarity Assessment of Cardinal Directions between Extended Spatial Objects. PHD Dissertation. The University of Maine,2000
[73] Liu X, Shekhar S, Chawla S. Consistency checking for Euclidean Spatial Constraints: A dimension graph approach technical report, TR00-039,Minneapolis: University of Minnesota, 2000
[74] 邓敏，李成名，刘文宝．利用拓扑和度量相结合的方法描述面向目标间的空间关系．测绘学报．2004，31(2)：164-169页
[75] 裴韬，周成虎等．空间数据知识发现研究进展概述．中国图象图形学报．2001，6(9)：854-860P
[76] 陈述彭，鲁学军，周成虎．地理信息导论[M]．北京：机械工业出版社，1999．
[77] 陈中祥，岳超源．空间数据挖掘的研究与发展．计算机工程与应用，2003，7(3)：5-7，33页
[78] Wu D, Hou YT, Zhang Y Q. Transporting Real-time Video over the Internet Challenges and Approaches. Proceeding of the IEEE, 2000, 88(12): 1855-1875P
[79] Shehroz S. Khan, Amir Ahmad. Cluster center initialization algorithm for K-Means clustering. Pattern Recognition Letters.2000, 25: 1293-1302P
[80] He J, Lan M, Tan C L, et al. Initialization of cluster refinement algorithms:a review and comparative study. In: Proceeding of International Joint Conference on Neural Networks. Budapest, 2004
[81] Forgy E. Cluster analysis of multivariate data: efficiency vs.interpretavility of classifications. In: Proceeding of WNAR meeting[C].Univ of California Riverside, 1995
[82] 牛琨，张舒博，陈俊亮．一种基于复合近邻的聚类中心点初始化算法．哈尔滨工程大学学报．2006，27(z)：379-383页
[83] 王汉芝，刘振全．一种新的确定K-均值算法初始聚类中心的方法[J]．天津科技大学学报，2005，20(4)：76-79页
[84] Shehroz S. Khan, Amir Ahmad. Cluster center initialization algorithm for K-Means clustering [J]. Pattern Recognition Letters. 2004, 25:1293-1302P
[85] 牛琨，张舒博，陈俊亮．融合网格密度的聚类中心初始化方案[J]．北京邮电大学学报，2005，30(2)：7-10页
[86] He Ji, Lan M, Tan C. L. Initialization of cluster refinement algorithms: a review and comparative study[C]. Proceeding of International Joint Conference on Neural Networks, Budapest, 2004: 297-302P
[87] Melli G. Dataset generator (datgen) [CP/OL].http://www.datasetgenerator.com/
[88] D.W. Scot. Multivariate Density Estimation. Wiley&Sons, 1992
[89] K Beyer, J. Goldsteein R. Ramakrishnan, U Shaft. When is Nearest Neighbors Meaningful? [A]. In: Proc. the 7th Int. Conf. Database Theory[C]. Jerusalem Israel, 1999:217-235P
[90] 陈慧萍，王煜，王建东．子空间聚类算法的研究新进展．计算机仿真．2007：24(3)：6-10，34页
[91] Rakesh Agrawal, Ramakrishnan Srikant. Fast algorithms for mining association rules in large databases[C]. In: Proc of the 20th Int Conf of Very Large Data Bases. San Francisco: Morgan Kaufmann, 1994:487-499P
[92] 谢坤武，毕晓玲，叶斌．基于单元区域的高维数据聚类算法．计算机研究与发展．2007，44(9)：1618-1623页
[93] OTOO EJ, SHOSHANI A, HWANG SW. Clustering High Dimensional Massive Science Datasets. Journal of Intelligent Information Systerm,2001,17(2/3): 147-168P
[94] 王生生，刘大有，曹斌，刘杰．一种高维空间数据的子空间聚类算法．计算机应用，2005，11(25)：2615-2617页
[95] Tung A K H, Hou Jean, Han Jiawei. Spatial Clustering in the Presence of Obstacles. Int. Conf. on Data Engineering. Heidelberg, Germany. 2001:359-367P
[96] 陈述彭，鲁学军，周成虎．地理信息导论[M]．北京：机械工业出版社，1999．
[97] Goodchild M F, Quattrochi D A. Scale Multiscaling, Remote Sensing and GIS. LEWIS Publishers. 1997: 1-14P
[98] 李霖，吴凡．空间数据多尺度表达模型及其可视化．北京：科学出版社．2005：40-45页
[99] A P Witkin. Scale-space filtering. Proc. 8th Int. Joint Conf. Art. Intell[C].1983: 1019-1022P
[100] 汪闽，周成虎，裴韬，骆剑承．MSCMO：基于数学形态学算子的尺度空间聚类算法．遥感学报．2004，8(1)：45-50页
[101] Romeny B M H, Florack L. A multiscale geometric model of human vision.In: Hendee W R, Wells P N T eds. The Perception of Wisual Information.New York: Springer-Verlag. 1993: 73-114P
[102] Romeny B M H, Florack L, Koenderink J, Viergever Meds. Scale-Space Theory in Computer Vision. Berlin Heidelberg: Springer-verlag. 1997
[103] 王艳慧．路网多尺度表达目标派生的内插方法研究[D]．北京：中国矿 业大学，2005．
[104] Sun Qing-xian, Fang Tao, Guo Da-zhi. Study on scale transformation in spatial data mining[C]. Proceedings of International Symposium on Spatio-temporal Modeling, Spatial Reasoning, Spatial Analysis, Data Mining and Data Fusion. Beijing: Peking University Press, 2005:275-279P
[105] Wang Shu-liang, Li De-ren. A perspective of spatial data mining[C].Geospatial Information, data mining and application. Wuhan: Wuhan University Press. 2005:604518 1-604518 10P
[106] 孙庆先，陈秋平，方涛，郭达志，杨可明．基于模糊聚类的多尺度空间数据挖掘模型及其矿山应用．上海交通大学学报．2008，42(2)：194-201页
[107] 张讲社，梁怡，徐宗本．基于视觉系统的聚类算法．计算机学报．2001，24(5)：496-501页
[108] 李松涛，张长水，荣钢，边肇祺．一种基于表面法向矢量的浓度图像分割方法．模式识别与人工智能．2000，13(4)：65-69页
[109] 赵艳厂，谢帆，宋俊德．一种新的聚类算法：等密度线算法．北京邮电大学学报．2002：25(2)：8-13页
[110] 赵艳厂，宋梅，谢帆，宋俊德．用于不同密度聚类的多阶段等密度线算法．北京邮电大学学报．2003：26(2)：42-47页
[111] Zhao Yanchang, Song Junde. AGRD: an efficient algorithm for clustering large high-dimensional datasets. Proc the 7th Pacific-Asia Conf on Knowledge Discovery and Data Mining (PAKDD-03).Seoul Korea, 2003,26(37): 271-282P
[2] Krzysztof, Kpperski, Junas Adhikary, Jiawei Han. Mining: Progress Workshop on and Challenges Survey Research Issues on paper[R]. Data Spatial Data SIGMOD'06 Mining and Knowledge (DMKD'06), Canada:Montreal, 2006
[3] 毛克彪，覃志豪，李海涛，周若鸿．基于空间数据仓库的空间数据挖掘研究．遥感信息(理论研究)．2002：19-26页
[4] 张瑞菊，陶华学．GIS与空间数据挖掘集成问题的研究．勘察科学技术．2003，2(1)：21-24页
[5] 毛克彪，田庆久．空间数据挖掘技术方法及应用．遥感技术与应用，2002，17(4)：198-204页
[6] K.L. Kaufman, P.J. Rousseeuw. Finding Groups in Data: An introduction to Cluster Analysis, John Wiley & Sons. 1990.
[7] R. Ng, J, Han. Efficient and Effective Clustering Method for Spatial Data Mining. Proc.1994 Int. Conf. Very Large Data Bases (VLDB'94), 1994:144-155P
[8] T. Zhang, R. Ramakrishnan, M. Livny. Birch: An Efficient Data Clustering Method for Very Large Databases. Proc. 1996 ACM-SIGMOD Inc. Conf. Management of Data (SIGMOD'96), 1996: 103-114P
[9] S.Guha, R.Rastogi, K. Shim. CURE: An Efficient Clustering Algorithm for Large Databases. In Proc. of the ACM SIGMOD, 1998.
[10] G. Karypis, E.H. Han, V. Kumar. CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling. COMPUTER. 1999,2(32): 68-75P
[11] M. Ester, H. P. Kriegel, and X. Xu, A density-based algorithm for discovering clusters in large spatial databases. Knowledge Discovery and Data Mining, Portland, 1996: 226-23 IP
[12] A. Hinneburg and D. A. Keim, An efficient approach to clustering in large multimedia databases with noise. Knowledge Discovery and Data Mining, New York, 1998:58-65P
[13] M. nkerst, M. breunig, H. P. Kriegel, and J. Sander, Ordering points to identify the clustering structure. Management of Data, Philadelphia, 1999: 49-60P
[14] J. Wang, R. Yang, Muntz. STING: A Statistical Information Grid Approach to Spatial Data Mining. Proc. 1997 Int. Conf. Very Large Data Bases (VLDB'97). 1997: 186-195P
[15] X.Xu, M.Ester, H. P. Kriegel, et al. A Distribution-Based Clustering Algorithm for Mining in Large Spatial Databases. Proc. 1998 Int. Conf. Data Engineering. Orlando: IEEE Computer Society Presss. 1998: 324-331P
[16] R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, Automatic subspace clustering of high dimensional data for data mining applications. Management of Data, Seattle, 1998: 94-105P
[17] G. Sheikholeslami, S. Chatterjee, and A. Zhang, A multi-resolution clustering approach for very large spatial databases. Very Large Data Bases, New York, 1998: 428-439P
[18] Haofeng Zhou, Qingqing Yuan, Zunping Cheng, Baile Shi. PHC: A Fast Partition and Hierarchy based Clustering Algorithm. Journal of Computer Science and Technology. 2003, 18(3): 407-411P
[19] Ding Zhou, Zunping Cheng, Chen Wang, Haofeng Zhou, Wei Wang, Baile Shi. SUDEPHIC: Self-tuning Density based Partitioning and Hierarchical Clustering. In proceedings of the 9th International Conference on Database Systems for Advanced Applications (DASFAA'04). 2004
[20] Yu Qian, Kang Zhang. GraphZip: A Fast and Automatic Compression Method for Spatial Data Clustering. 2004 ACM Symposium on Applied Computing, 2004
[21] Fabrizio Angiulli, Clara Pizzuti, Massimo Ruffolo. DESCRY: A Grid and Density based Clustering Algorithm for Very Large Data Sets. In proceedings of the 5th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL'04). 2004: 8:25-27P
[22] J. Vitter. Random sampling with a reservoir. CM Transaction on Mathematical Software. 1995,11(1): 37-57P
[23] J.E.Jackson. A User's Guide To Principal Components. John Wiley&Sons, 1991.
[24] A. K. Jain, R. C. Dubes. Algorithms for Clustering Data. Prentice Hall, 1998.
[25] T. Kohonen. Self-Organization and Associated Memory. Springer-Verlag, 1998.
[26] G. Sheikholeslani, S.Chatterjee, A. Zhang. WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases. In Proceedings of the 24th VLDB conference. 1998.
[27] Chen Ning, Chen An, Zhou Long-xiang. An Effective Clustering Algorithm in Large Tansaction Databases. Softwares Journal. 2001, 12(4): 475-484P
[28] Joliffe, I. Principal Component Analysis. Springer-Verlag. NewYork, NY, 1996.
[29] M. Wberry, S.T. dumais, G. W. O'Brien. Using linear algebra for intelligent information retrieval. SIAM Revies. 1995, 37:573-595.
[30] Keogh E., Chakrabarti K., Pazzani M., Mehrotra, S.. Dimensionality reduction for fast similarity search in large time series database. Journal of Knowledge and Information System. 2001.
[31] Cheng, C., Fu, A., Zhang, Y. Entropy-based subspace clustering for mining numerical data. Proceedings in ACM SIGKDD. San Diego, CA .1999: 84-93P
[32] Goil, S., Nagesh, H., Choudhary, A. MAFIA: Efficient and scalable subspace clustering for very large data sets. Technical report CPDC-TR-9906-010. Northwestern University. 1999
[33] 刘勘，周晓峥，周洞汝．一种基于排序子空间的高维聚类算法及其可视化研究．计算机研究与发展．2003，40(10)：1509-1513页
[34] 王生生，刘大有，曹斌，刘杰．一种高维空间数据的子空间聚类算法．计算机应用．2005，11(25)：2615-2617页
[35] A. H. Pilevar, M. Sukumar. GCHL: A grid-clustering algorithm for high-dimensional very large spatial data bases. Pattern Recognition Letters.2005, 11(26): 999-1010P
[36] A.K.H. Tung, J. Hou, J. Han. Spatial Clustering in the Presence of Obstacles. Int. Conf. on Data Engineering. Heidelberg, Germany. 2001:359-367P
[37] V. Estivill Castro, I. J. Lee. AutoClust+: Automatic Clustering of Point-Data Sets in the Presence of Obstacles. Int. Workshop on Temporal,Spatial and Spatio-Temporal Data Mining. Lyon, France. 2000: 133-146P
[38] V. Estivill Castro, I. J. Lee. AutoClust: Automatic Clustering via Boundary Extraction for Massive Point-Data Sets. Int. Conf. On Geo-computation'00.2000: 23-25P
[39] O.R. Zaiane, C. H. Lee. Clustering Spatial Data When Facing Physical Constraints. Int. the IEEE International Conf. On Data Mining. Maebashi City, Japan. 2002: 737-740P
[40] X. Wang, H. J. Hamilton. Density-Based Spatial Clustering in the Presence of Obstacles and Facilitators. Int. the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases. Pisa, Italy.2004: 446-458P
[41] X. Wang, H. J. Hamilton. DBRS: A Density-Based Spatial Clustering Method with Random Sampling. Proc. the 7th International Conference on PAKDD. Seoul, Korea. 2003: 563-575P
[42] Hai-Dong Yang, Fei-qi Deng. The Study on Immune Spatial Clustering Model based on Obstacle. Int. the 3th International Conference on Machine Learning and Cybernetics. Shanghai, China. 2004: 378-382P
[43] Leung Y. Zhang J. Xu Z. Clustering by scale-space filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22:1396-1410P
[44] 汪闽．空间数据挖掘方法研究．中国科学院地理科学与资源研究所博士论文，2003
[45] 邸凯昌，李德仁，李德毅．从空间数据库发现聚类：一种基于数学形态学的算法．中国图像图形学报．1998，3(3)：173-178页
[46] 陈江平，李平湘．点对象的多尺度空间关联规则挖掘算法研究．计算机应用．2004，24(7)：18-21页
[47] 张文君，顾行发，陈良富等．基于均值-标准差的k均值初始聚类中心选取算法．遥感学报．2006，10(5)：715-721页
[48] K. Beyer, J. Goldstein, R. Ramakhrisnan, et al. Nearest neighbor' meaningful. Proc. the 7th Int. Conf. Database Theory (ICDT'99).http://citeseer.ist.psu.edu/605885.html.1999
[49] A. Hinneburg, C. C. Aggarwal, D. A. Keim. What is the nearest neighbor in high dimensional spaces. Proc. the 26th Int. Conf. Very Large Data Bases. San Francisco. 2000
[50] 汪闽，周成虎，裴韬，骆剑承．MSCMO：基于数学形态学算子的尺度空间聚类算法．遥感学报．2004，8(1)：45-50页
[51] 沙占江，马海州，李玲琴等．多尺度空间分层聚类算法在土地利用与土地覆被研究中的应用．地理科学．2004，24(4)：477-483页
[52] 李小斌，田铮．基于谱聚类的图像多尺度随机树分割．中国科学E辑：信息科学．2007，37(8)：1073-1085P
[53] Takuya Tsuji, Akihito Ito, Toshitsugu Tanaka. Multi-scale of clustering particles. Powder Technology. 2007, 179(8): 115-125P
[54] D.Y. Tzou, J.K. Chen, R. Roybal, J.E. Beraun. Cluster potentials for multi-scale interactions. International Journal of Heat and Mass Transfer.2004, 47: 2949-2959P
[55] Yan Jin. Ling Wang, Yoohwan Kim, Xiaozong Yang. EEMC: An energy-efficient multi-level clustering algorithm for large-scale wireless sensor networks. Computer Networks 2007, 10(5): 1-21P
[56] 张云涛，龚玲．数据挖掘原理与技术．北京：电子工业出版社，2004：35-41页
[57] Eliseo Clementini, P D Felice. Mining multiple-level spatial association rules for objects with a broad boundary. Data & Knowledge Engineering.2000, 34(2): 251-270P
[58] 刘大有，王生生，虞强源，胡鹤．基于定性空间推理的多层空间关联规则挖掘算法．计算机研究与发展，2004，41(4)：565-570页
[59] 袁红春，熊范纶．元规则指导下的逐步求精多层空间关联规则挖掘算法．计算机工程，2004，30(8)：33-36，39页
[60] Vladimir, Estivill-Castro, Ickjai Lee. Spatial Clustering with obstacles for Geo-graphical Data Mining. ISPRS Journal of Photogrammetry & Remote Sensing. 2004, 59(5): 21-34P
[61] 孙建国，赵军，闫浩文．一个空间数据挖掘原型系统的设计与实现．兰州交通大学学报，2004，23(6)：74-79页
[62] 邹力琨，王丽珍，何倩等．空间数据挖掘发展研究．计算机工程与应用，2003，11(2)：186-188页
[63] 袁红春，熊范纶，杭小树等．一个适用于地理信息系统的数据挖掘工具-GIS Miner．中国科学技术大学学报，2002，32(4)：217-224页
[64] A.K. Jain, R. C. Dubes. Algorithms for Clustering Data. Prentice Hall,1998
[65] Qian Wei-ning, Zhou Ao-ying. Analyzing Popular Clustering Algorithms from diffrernt Viewpoints. Journal of software, 2002, 13(8): 148-151P
[66] Martin Ester, Alexander Frommelt, Hans Peter kriegel. Spatial Data Mining: Database Primitives. Algorithms and Efficient DBMS Support Data Mining and Knowledge Discovery. 2000, 10(4): 193-216P
[67] 杨春成．空间数据挖掘中聚类分析算法的研究．解放军信息工程大学．博士学位论文．2004：1-4页．
[68] 汪闽．空间数据挖掘方法研究．中国科学院地理科学与资源研究所博士论文，2003．
[69] Yuhang Wang, Pillia Makedon, Amit Chakrabarti, R O-Histogram:Efficient representation of spatial relations between objects of arbitrary topology. Proceedings of the 12th annual ACM international conference on Multimedia. USA, 2004:10-16P
[70] 赵军喜，孙庆辉，张毅．GIS中几何对象之间的空间关系．测绘学院学报．2002，19(4)：306-309页
[71] 郭薇，陈军．三位拓扑空间关系的形式化描述及拓扑空间关系最小集．武汉水利电力大学学报．1996，29(4)：85-90页
[72] Goyal R.K..Similarity Assessment of Cardinal Directions between Extended Spatial Objects. PHD Dissertation. The University of Maine,2000
[73] Liu X, Shekhar S, Chawla S. Consistency checking for Euclidean Spatial Constraints: A dimension graph approach technical report, TR00-039,Minneapolis: University of Minnesota, 2000
[74] 邓敏，李成名，刘文宝．利用拓扑和度量相结合的方法描述面向目标间的空间关系．测绘学报．2004，31(2)：164-169页
[75] 裴韬，周成虎等．空间数据知识发现研究进展概述．中国图象图形学报．2001，6(9)：854-860P
[76] 陈述彭，鲁学军，周成虎．地理信息导论[M]．北京：机械工业出版社，1999．
[77] 陈中祥，岳超源．空间数据挖掘的研究与发展．计算机工程与应用，2003，7(3)：5-7，33页
[78] Wu D, Hou YT, Zhang Y Q. Transporting Real-time Video over the Internet Challenges and Approaches. Proceeding of the IEEE, 2000, 88(12): 1855-1875P
[79] Shehroz S. Khan, Amir Ahmad. Cluster center initialization algorithm for K-Means clustering. Pattern Recognition Letters.2000, 25: 1293-1302P
[80] He J, Lan M, Tan C L, et al. Initialization of cluster refinement algorithms:a review and comparative study. In: Proceeding of International Joint Conference on Neural Networks. Budapest, 2004
[81] Forgy E. Cluster analysis of multivariate data: efficiency vs.interpretavility of classifications. In: Proceeding of WNAR meeting[C].Univ of California Riverside, 1995
[82] 牛琨，张舒博，陈俊亮．一种基于复合近邻的聚类中心点初始化算法．哈尔滨工程大学学报．2006，27(z)：379-383页
[83] 王汉芝，刘振全．一种新的确定K-均值算法初始聚类中心的方法[J]．天津科技大学学报，2005，20(4)：76-79页
[84] Shehroz S. Khan, Amir Ahmad. Cluster center initialization algorithm for K-Means clustering [J]. Pattern Recognition Letters. 2004, 25:1293-1302P
[85] 牛琨，张舒博，陈俊亮．融合网格密度的聚类中心初始化方案[J]．北京邮电大学学报，2005，30(2)：7-10页
[86] He Ji, Lan M, Tan C. L. Initialization of cluster refinement algorithms: a review and comparative study[C]. Proceeding of International Joint Conference on Neural Networks, Budapest, 2004: 297-302P
[87] Melli G. Dataset generator (datgen) [CP/OL].http://www.datasetgenerator.com/
[88] D.W. Scot. Multivariate Density Estimation. Wiley&Sons, 1992
[89] K Beyer, J. Goldsteein R. Ramakrishnan, U Shaft. When is Nearest Neighbors Meaningful? [A]. In: Proc. the 7th Int. Conf. Database Theory[C]. Jerusalem Israel, 1999:217-235P
[90] 陈慧萍，王煜，王建东．子空间聚类算法的研究新进展．计算机仿真．2007：24(3)：6-10，34页
[91] Rakesh Agrawal, Ramakrishnan Srikant. Fast algorithms for mining association rules in large databases[C]. In: Proc of the 20th Int Conf of Very Large Data Bases. San Francisco: Morgan Kaufmann, 1994:487-499P
[92] 谢坤武，毕晓玲，叶斌．基于单元区域的高维数据聚类算法．计算机研究与发展．2007，44(9)：1618-1623页
[93] OTOO EJ, SHOSHANI A, HWANG SW. Clustering High Dimensional Massive Science Datasets. Journal of Intelligent Information Systerm,2001,17(2/3): 147-168P
[94] 王生生，刘大有，曹斌，刘杰．一种高维空间数据的子空间聚类算法．计算机应用，2005，11(25)：2615-2617页
[95] Tung A K H, Hou Jean, Han Jiawei. Spatial Clustering in the Presence of Obstacles. Int. Conf. on Data Engineering. Heidelberg, Germany. 2001:359-367P
[96] 陈述彭，鲁学军，周成虎．地理信息导论[M]．北京：机械工业出版社，1999．
[97] Goodchild M F, Quattrochi D A. Scale Multiscaling, Remote Sensing and GIS. LEWIS Publishers. 1997: 1-14P
[98] 李霖，吴凡．空间数据多尺度表达模型及其可视化．北京：科学出版社．2005：40-45页
[99] A P Witkin. Scale-space filtering. Proc. 8th Int. Joint Conf. Art. Intell[C].1983: 1019-1022P
[100] 汪闽，周成虎，裴韬，骆剑承．MSCMO：基于数学形态学算子的尺度空间聚类算法．遥感学报．2004，8(1)：45-50页
[101] Romeny B M H, Florack L. A multiscale geometric model of human vision.In: Hendee W R, Wells P N T eds. The Perception of Wisual Information.New York: Springer-Verlag. 1993: 73-114P
[102] Romeny B M H, Florack L, Koenderink J, Viergever Meds. Scale-Space Theory in Computer Vision. Berlin Heidelberg: Springer-verlag. 1997
[103] 王艳慧．路网多尺度表达目标派生的内插方法研究[D]．北京：中国矿 业大学，2005．
[104] Sun Qing-xian, Fang Tao, Guo Da-zhi. Study on scale transformation in spatial data mining[C]. Proceedings of International Symposium on Spatio-temporal Modeling, Spatial Reasoning, Spatial Analysis, Data Mining and Data Fusion. Beijing: Peking University Press, 2005:275-279P
[105] Wang Shu-liang, Li De-ren. A perspective of spatial data mining[C].Geospatial Information, data mining and application. Wuhan: Wuhan University Press. 2005:604518 1-604518 10P
[106] 孙庆先，陈秋平，方涛，郭达志，杨可明．基于模糊聚类的多尺度空间数据挖掘模型及其矿山应用．上海交通大学学报．2008，42(2)：194-201页
[107] 张讲社，梁怡，徐宗本．基于视觉系统的聚类算法．计算机学报．2001，24(5)：496-501页
[108] 李松涛，张长水，荣钢，边肇祺．一种基于表面法向矢量的浓度图像分割方法．模式识别与人工智能．2000，13(4)：65-69页
[109] 赵艳厂，谢帆，宋俊德．一种新的聚类算法：等密度线算法．北京邮电大学学报．2002：25(2)：8-13页
[110] 赵艳厂，宋梅，谢帆，宋俊德．用于不同密度聚类的多阶段等密度线算法．北京邮电大学学报．2003：26(2)：42-47页
[111] Zhao Yanchang, Song Junde. AGRD: an efficient algorithm for clustering large high-dimensional datasets. Proc the 7th Pacific-Asia Conf on Knowledge Discovery and Data Mining (PAKDD-03).Seoul Korea, 2003,26(37): 271-282P