空间数据挖掘的研究
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摘要
空间数据挖掘是指从空间数据库中提取用户感兴趣的空间模式与特征、空间与非空间数据的普遍关系及其它一些隐含在空间数据中的普遍的数据特征。本文系统研究了空间数据挖掘的理论、方法和应用。主要内容有：
(1)建立起空间数据挖掘的基础理论和技术框架，进一步完善了空间数据挖掘的理论和方法。阐述了空间数据挖掘的定义与特点，提出一种包括数据源、挖掘器、用户界面三层结构的空间数据挖掘体系结构，阐述空间数据挖掘的基本步骤和从空间数据库中能发现的九种知识类型，系统研究了17种空间数据挖掘方法，阐述了各种方法的特点和适用范围，阐述了空间数据挖掘与其它相关学科的区别与联系，指出空间数据挖掘的主要研究方向，提出开发空间数据挖掘系统的几条原则。
(2)将粗集理论引入空间数据挖掘领域，系统地研究了粗集理论用于空间数据挖掘的基础理论和方法，包括粗集的基本概念和性质、粗集的扩展模型、空间数据库中知识表达系统的分类、属性表的一致性分析、属性的依赖关系和属性的重要性、决策表属性约简和属性值约简等。
(3)提出空间关联规则主要指空间对象之间的空间和非空间关系，指出空间关联规则的形式十分丰富，重点研究了两种形式的空间关联规则的挖掘。阐述了A=>B[s％，c％]形式的空间关联规则的基本概念和算法，详细研究了一种逐步求精的空间关联规则挖掘算法的实现；提出一种基于空间数据立方体的空间关联规则挖掘的新思路；将空间统计分析引入空间关联规则挖掘领域，研究了空间权重矩阵、空间自相关、空间关联等的度量函数，并利用空间统计分析技术发现空间相关关系和空间关联规则。
(4)系统研究了七种典型的空间数据聚类方法，积极探索基于约束条件的空间聚类问题的解决方案；将遗传算法引入空间数据聚类领域，提出一种基于遗传算法的空间聚类算法，该算法兼顾了局部收敛和全局收敛性能。
(5)系统研究了Voronoi图和Delaunay三角网的定义、性质及各种建立算法，并对它们在空间数据挖掘中的应用进行了探索性研究：提出Voronoi图是界定空间目标(现象)的空间影响范围的一种行之有效的办法；从理论上论证了Voronoi图的形成与城市中心地的形成是一致的，提出Delaunay三角网是建立城镇网络体系的最佳模型；研究了利用Voronoi图进行公共设施选址优化的算法及实现。
Spatial data mining (SDM) refers to picking up interesting rules from spatial database, such as spatial patterns and characteristics, the universal relations of spatial and non-spatial data and other universal implicated in spatial data. This thesis studies on the theories, techniques and the applications of SDM. The main content of this paper include the follows:
(1) The basic theory and technology framework of spatial data mining are established and the theory and methods are perfectly developed. The definition and characteristics of SDM are set forth, and a structure of spatial data mining system including data source, miner and user interface is put forward. The essential processes of SDM are studied and nine types of rules resulting in mining are discussed. There are 17 kinds of spatial data mining approaches researched in this paper and each method's characteristics are analyzed. Moreover, the difference and relationship between SDM and other related subjects are discussed in detail. The future research directions of SDM and some principles on developing SDM system are pointed out.
(2) The theory of Rough Sets is introduced into SDM domain. The basal theory and techniques of Rough Sets including the basic notion and character, the extended models, the classification of knowledge expression system in spatial database, the coherence analysis of attribute table, the relying relations between attributes, the importance of attribute, the reduction of attribute and the reduction of attribute value in decision table are studied by the numbers.
(3) The definition of spatial association rule is defined as the spatial and non-spatial relations between spatial objects. The forms of spatial association rule are abundant. Two important types of spatial association rule are studied. Firstly, The notion of the form as A=>B[s%, c%] is researched and some algorithms are discussed. An algorithm named A Progressive Refinement Approach to Spatial Data Mining is discussed in detail. And a new thought of mining spatial association rule based on spatial data cube is brought forward. Then, the spatial statistical analysis techniques are introduced into SDM domain. The measurement functions of spatial weight matrix, spatial auto-correlation and spatial association are studied.
(4) Seven kinds of spatial data clustering approaches are studied. And the technique to solve the problem of Constraint-based Spatial Cluster Analysis is explored. In addition, a new spatial clustering algorithm based on Genetic Algorithms is set forward and it can give attention to local constringency and the whole constringency.
(5) The definitions, characteristics and all kinds of building algorithms of the Voronoi Diagram and the Delaunay Triangle are introduced. Their applications in SDM are explored. That the Voronoi Diagram is an effective method to partition the influence regions between spatial objects and phenomena is put forward, and that the principle of building Voronoi Diagram is identical to the forming central place is proved. Then, that the Delaunay Triangle is the best model to set up the cities network is brought forward. Finally, the problem of spatial establishment location selection by means of the Voronoi Diagram is studied.
(1)建立起空间数据挖掘的基础理论和技术框架，进一步完善了空间数据挖掘的理论和方法。阐述了空间数据挖掘的定义与特点，提出一种包括数据源、挖掘器、用户界面三层结构的空间数据挖掘体系结构，阐述空间数据挖掘的基本步骤和从空间数据库中能发现的九种知识类型，系统研究了17种空间数据挖掘方法，阐述了各种方法的特点和适用范围，阐述了空间数据挖掘与其它相关学科的区别与联系，指出空间数据挖掘的主要研究方向，提出开发空间数据挖掘系统的几条原则。
(2)将粗集理论引入空间数据挖掘领域，系统地研究了粗集理论用于空间数据挖掘的基础理论和方法，包括粗集的基本概念和性质、粗集的扩展模型、空间数据库中知识表达系统的分类、属性表的一致性分析、属性的依赖关系和属性的重要性、决策表属性约简和属性值约简等。
(3)提出空间关联规则主要指空间对象之间的空间和非空间关系，指出空间关联规则的形式十分丰富，重点研究了两种形式的空间关联规则的挖掘。阐述了A=>B[s％，c％]形式的空间关联规则的基本概念和算法，详细研究了一种逐步求精的空间关联规则挖掘算法的实现；提出一种基于空间数据立方体的空间关联规则挖掘的新思路；将空间统计分析引入空间关联规则挖掘领域，研究了空间权重矩阵、空间自相关、空间关联等的度量函数，并利用空间统计分析技术发现空间相关关系和空间关联规则。
(4)系统研究了七种典型的空间数据聚类方法，积极探索基于约束条件的空间聚类问题的解决方案；将遗传算法引入空间数据聚类领域，提出一种基于遗传算法的空间聚类算法，该算法兼顾了局部收敛和全局收敛性能。
(5)系统研究了Voronoi图和Delaunay三角网的定义、性质及各种建立算法，并对它们在空间数据挖掘中的应用进行了探索性研究：提出Voronoi图是界定空间目标(现象)的空间影响范围的一种行之有效的办法；从理论上论证了Voronoi图的形成与城市中心地的形成是一致的，提出Delaunay三角网是建立城镇网络体系的最佳模型；研究了利用Voronoi图进行公共设施选址优化的算法及实现。
Spatial data mining (SDM) refers to picking up interesting rules from spatial database, such as spatial patterns and characteristics, the universal relations of spatial and non-spatial data and other universal implicated in spatial data. This thesis studies on the theories, techniques and the applications of SDM. The main content of this paper include the follows:
(1) The basic theory and technology framework of spatial data mining are established and the theory and methods are perfectly developed. The definition and characteristics of SDM are set forth, and a structure of spatial data mining system including data source, miner and user interface is put forward. The essential processes of SDM are studied and nine types of rules resulting in mining are discussed. There are 17 kinds of spatial data mining approaches researched in this paper and each method's characteristics are analyzed. Moreover, the difference and relationship between SDM and other related subjects are discussed in detail. The future research directions of SDM and some principles on developing SDM system are pointed out.
(2) The theory of Rough Sets is introduced into SDM domain. The basal theory and techniques of Rough Sets including the basic notion and character, the extended models, the classification of knowledge expression system in spatial database, the coherence analysis of attribute table, the relying relations between attributes, the importance of attribute, the reduction of attribute and the reduction of attribute value in decision table are studied by the numbers.
(3) The definition of spatial association rule is defined as the spatial and non-spatial relations between spatial objects. The forms of spatial association rule are abundant. Two important types of spatial association rule are studied. Firstly, The notion of the form as A=>B[s%, c%] is researched and some algorithms are discussed. An algorithm named A Progressive Refinement Approach to Spatial Data Mining is discussed in detail. And a new thought of mining spatial association rule based on spatial data cube is brought forward. Then, the spatial statistical analysis techniques are introduced into SDM domain. The measurement functions of spatial weight matrix, spatial auto-correlation and spatial association are studied.
(4) Seven kinds of spatial data clustering approaches are studied. And the technique to solve the problem of Constraint-based Spatial Cluster Analysis is explored. In addition, a new spatial clustering algorithm based on Genetic Algorithms is set forward and it can give attention to local constringency and the whole constringency.
(5) The definitions, characteristics and all kinds of building algorithms of the Voronoi Diagram and the Delaunay Triangle are introduced. Their applications in SDM are explored. That the Voronoi Diagram is an effective method to partition the influence regions between spatial objects and phenomena is put forward, and that the principle of building Voronoi Diagram is identical to the forming central place is proved. Then, that the Delaunay Triangle is the best model to set up the cities network is brought forward. Finally, the problem of spatial establishment location selection by means of the Voronoi Diagram is studied.
引文
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[119] 《河南省城市化发展战略研究》2001年12月．河南省发展计划委员会规划处白皮书
[120] 《河南省“十五计划”》．
[121] Alex Berson, Stephen J. Smith. Data Warehousing, Data Mining, &OLAP[M]. McGraw-Hill Book Co, 1997.
[122] A. K. H. Tung, J. Hou, and J. Han. Spatial Clustering in the Presence of Obstacles [A], Proc.2001 Int. Conf.on Data Engineering (ICDE'01)[C], Heidelberg, Germany, April 2001.
[123] A.K.H.Tung, J.Han, L.V.S. Lakshmanan, and R.T.Ng. Constraint-Based Clustering in Large Databases [A], Proc. Int. Conf.on Database Theory(ICDT'01)[C], London, U.K., Jan.2001, 405-419.
[124] A.K.H.Tung, R.T.Ng, L.V.S.Lakshmanan, and J.Han. Geo-spatial Clustering with User-Specified Constraints[A]. Proceedings of the International Workshop on Multimedia Data Mining (MDM/KDD'2000) [C], in conjunction with ACM SIGKDD conference. Boston, USA, August 20, 2000
[125] A.K.H.Tung, H.Lu, J.Han, and L.Feng. Breaking the Barrier of Transactions: Mining Inter-Transaction Association Rules [A], Proc. 1999 Int.Conf.on Knowledge Discovery and Data Mining (KDD'99)[C], San Diego, CA, 1999.
[126] Betty Bin Xia, Similarity Search in Time Series Data Sets[D], M.Sc, thesis, Computing Science,
Simon Fraser University, December 1997.
[127] Benjamin Xuebin Lu. Fast Computation of Sparse Data Cubes and its Applications [D], M.Sc.thesis, Computing Science, Simon Fraser University, Dec.2000.
[128] D.Cheung, J. Han, V.Ng and C. Y. Wong, Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique[A], Proc. of 1996 Int'l Conf. on Data Engineering (ICDE'96)[C], New Orleans, Louisiana, USA, 1996.
[129] David J. Abel, Beng Chin Cooi, Kian-Lee Tan, Towards integrated geographic information processing[J], Geographical Information Science, 1998(4)
[130] Ester M, Kriegel H P, Jrg Sander, Xiaowei Xu. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise[A]. Proceedings of 2rid International Conference on Knowledge Discovery and Data Mining (KDD-96) [C].
[131] Ester M, Frommelt A, Hans-Peter Kriegel, Jrg Sander. Algorithms for Characterization and Trend Detection in Spatial Databases[A]. Proceedings of 4th International Conference on Knowledge Discovery and Data Mining (KDD-98) [C].
[132] Edited by Harvey J. Miller, J. Han, Geographic Data Mining and Knowledge Discovery[M], London and NewYork, TAYLOR & FRANCIS, 2001.
[133] Georges G. Grinstein, Matthew O. Ward, Introduction to Data Visualization[A], from Information Visualization in Data Mining and Knowledge Discovery[C], ACADEMIC PRESS, 2002, pp.21-46
[134] Gore, A., The Digital Earth, Understanding our planet in 21st Century[J], the Australian Surveyor, Vol.43, No.2, 1998
[135] H. Lu, L. Feng, and J. Han, Beyond Intra-Transaction Association Analysis: Mining Multi-Dimensional Inter-Transaction Association Rules [J], ACM Transactions on Information Systems, 18(4), 2000, pp.423-454.
[136] Jagadish, H. V. and Ng.R, Incompleteness in Data Mining[A], Proceedings SIGMOD Workshop on Research Issues on Data Mining[C], May 2000, pp. 1-10.
[137] J. Han, M. Kamber, andA. K. H. Tung, Spatial Clustering Methods in Data Mining: A Survey [A], H. Miller and J.Han (eds.), { Geographic Data Mining and Knowledge Discovery[C]}, Taylor and Francis, 2001.
[138] J. Han, N. Stefanovic, and K. Koperski, Selective Materialization: An Efficient Method for Spatial Data Cube Construction[A], Proc. 1998 Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD'98)[C], Melboume, Australia, April 1998, pp.144-158.
[139] J. Han, K. Koperski, and N. Stefanovic, GeoMiner: A System Prototype for Spatial Data Mining[A], Proc. 1997 ACM-SIGMOD Int'l Conf. on Management of Data(SIGMOD'97) [C], Tucson, Arizona, May 1997.
[140] J. Han and J. Pei. Mining Frequent Patterns by Pattern-Growth: Methodology and Implications [J], ACM SIGKDD Explorations,2(2), December 2000.
[141] J. Han and Y. Fu, Discovery of Multiple-Level Association Rules from. Large Databases [J], IEEE Transactions on Knowledge and Data Engineering, 11(5), 1999.
[142] J. Han, L.V.S. Lakshmanan, and R. T. Ng, Constraint-Based, Multidimensional Data Mining [J], COMPUTER (special issues on Data Mining), 1999, 32(8): pp.46-50.
[143] J. Han, Y. Fu, W. Wang, J. Chiang, O.R.Zaiane, and K.Koperski, DBMiner: Interactive Mining of Multiple-Level Knowledge in Relational Databases[A], Proc. 1996 ACM-SIGMOD Int'l Conf. on Management of Data (SIGMOD'96) [C], Montreal, Canada, June 1996.
[144] J. Han, O. R. Zaiane, and Y. Fu, Resource and Knowledge Discovery, in Global Information Systems." A Scalable Multiple Layered Database Approach[A], Proc.of a Forum on Research and Technology Advances in Digital Libraries (ADL'95) [C], McLean, Virginia, 1995.
[145] J. Han, OLAP Mining: An Integration of OLAP with Data Mining[A], Proc. 1997 IFIP Conference on Data Semantics (DS-7) [C], Leysin, Switzerland, Oct. 1997, pp.1-11.
[146] J. Han, J. Pei, G. Dong, and K. Wang, Efficient Computation of Iceberg Cubes with Complex Measures [A], Proc.2001 ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD'01) [C], Santa Barbara, CA, May 2001.
[147] J. Han, H. Jamil, Ying Lu, Liangyou Chen, Y.Liao, and J.Pei, DNA-Miner: A System Prototype for Mining DNA Sequences [A], Proc. 2001 ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD'01)[C], Santa Barbara, CA, May 2001.
[148] J. Han, G. Dong, and Y. Yin, Efficient Mining of Partial Periodic Patterns in Time Series Database [A], Proc. 1999 Int. Conf. on Data Engineering (ICDE'99) [C], Sydney, Australia, March 1999, pp. 106-115.
[149] J.Han, Characteristic Rules [A], DBMiner, to appear in W. Kloesgen and J. Zytkow (eds.),Handbook of Data Mining and Knowledge Discovery[M], Oxford University Press, 1999.
[150] J. Han, W. Gong, and Y. Yin, Mining Segment-Wise Periodic Patterns in Time-Related Databases [A], Proc. of 1998 Int'l Conf. on Knowledge Discovery and Data Mining (KDD'98) [C], New York City, NY, Aug. 1998, pp.214-218.
[151] J.Han, Yongjian Fu, Wei Wang, Jenny Chiang, Wan Gong, Krzysztof Koperski, Deyi Li, Yijun Lu, Amynmohamed Rajan, Nebojsa Stefanovic, Betty Xia, Osmar R. Zaiane, DBMiner: A System for Mining Knowledge in Large Relational Databases [A], Proc. 1996 Int'l Conf. on Data Mining and Knowledge Discovery(KDD'96) [C], Portland, Oregon, 1996, pp. 250-255.
[152] J. Han, Data Mining Techniques[A], Proc. 1996 ACM-SIGMOD Int'l Conf. on Management of Data (SIGMOD'96) [C], Montreal, Canada, June 1996.
[153] J. Han, Y. Fu, K. Koperski, W.Wang, and O.Zaiane, DMQL: A Data Mining Query Language for Relational Databases[A], 1996 SIGMOD'96 Workshop. on Research Issues on Data Mining and Knowledge Discovery(DMKD'96) [C], Montreal, Canada, June 1996.
[154] J. Han and Y. Fu, Exploration of the Power of Attribute-Oriented Induction in Data Mining[A], U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (eds.), Advances in Knowledge Discovery and Data Mining[C], AAAI/MIT Press, 1996, pp. 399-421.
[155] J. Han, Mining Knowledge at Multiple Concept Levels[A], Proc. 4th Int'l Conf. on Information and Knowledge Management (CIKM'95) [C], Baltimore, Maryland, 1995, pp.19-24.
[156] J.Han and Y. Fu, Discovery of Multiple-Level Association Rules from Large Databases[A], Proc. 1995 Int'l Conf. on Very Large Data Bases (VLDB'95) [C], Zürich, Switzerland, September 1995, pp.420-431.
[157] J.Han, Y. Fu and S. Tang, Advances of the DBLearn System for Knowledge Discovery in Large Databases[A], Proc.1995 Int'l Joint Conf. on Artificial Intelligence (IJCAI'95) [C], Montreal, Canada, 1995, pp.2049-2050.
[158] J.Han, From Database Systems to Knowledge-Base Systems: An Evolutionary Approach[A], Tutorial Notes at the 11th Int'l Conf. on Data Engineering (ICDE'95) [C],Taipei, Taiwan, 1995, pp. 1-106.
[159] J.Han, Y.Huang, N. Cercone, and Y.Fu, Intelligent Query Answering by Knowledge Discovery Techniques[J], IEEE Transactions on Knowledge and Data Engineering, 8(3), 1996,pp.373-390.
[160] J. Han, S. Nishio and H. Kawano, Knowledge Discovery in Object-Oriented and Active Databases[A], F. Fuchi and T. Yokoi (eds.), Knowledge Building and Knowledge Sharing, Ohmsha[C], Ltd.and IOS Press, 1994, pp.221-230.
[161] J. Han and Y. Fu, Dynamic Generation and Refinement of Concept Hierarchies for Knowledge Discovery in Databases[A], AAAI'94 Workshop on Knowledge Discovery in Databases (KDD'94)[C], Seattle, WA, 1994, pp.157-168.
[162] J. Han, Y. Fu, and R. Ng, Cooperative Query Answering Using Multiple Layered Databases[A], Proc. 2nd Int'l Conf. on Cooperative Information Systems (CoopIS'94)[C], Toronto, Canada, 1994, pp.47-58.
[163] J. Han, Y.Cai and N.Cercone, Knowledge Discovery in Databases: An Attribute-Oriented Approach[A], Proc. 1992 Int'l Conf. on Very Large Data Bases (VLDB'92)[C], Vancouver, Canada, 1992, pp.547-559.
[164] J. Han & Micheline Kamber, Data Mining: Concepts and Techniques[M]. Morgan Kaufmann Publishers, 2000.
[165] J.Pei, J.Han, H. Lu, S.Nishio, S.Tang, and D. Yang, H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases [A], Proc.2001 Int. Conf. on Data Mining (ICDM'01)[C], San Jose, CA, Nov. 2001.
[166] J.Pei, A.K.H.Tung, and J.Han, Fault-Tolerant Frequent Pattern Mining: Problems and Challenges [A], Proc. 2001 ACM-SIGMOD Int. Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD'01)[C], Santa Barbara, CA, May 2001.
[167] J.Pei, J.Han, and L.V.S.Lakshmanan, Mining Frequent Itemsets with Convertible Constraints [A], Proc. 2001 Int. Conf. on Data Engineering (ICDE'01)[C], Heidelberg, Germany, 2001.
[168] J.Pei and J.Han, Can We Push More Constraints into Frequent Pattern Mining? [A], Proc. 2000 Int. Conf. on Knowledge Discovery and Data Mining (KDD'00) [C], Boston, MA, August 2000.
[169] K. Koperski, J. Adhikary and J. Han, Spatial Data Mining: Progress and Challenges[A], 1996 SIGMOD'96 Workshop.on Research Issues on Data Mining and Knowledge Discovery (DMKD'96)[C], Montreal, Canada, 1996.
[170] K.Koperski. A Progress Refinement Approach to Spatial Data Mining[D]. PhD dissertation of Simon Fraser University, 1999.
[171] K. Koperski and J. Han, Discovery of Spatial Association Rules in Geographic Information Databases[A], Proc. 4th Int'l Symp. on Large Spatial Databases (SSD95)[C], Maine, Aug. 1995, pp. 47-66.
[172] K. Koperski, J. Han, and N. Stefanovic, An Efficient Two-Step Method for Classification of Spatial Data[A], Proc. 1998 International Symposium on Spatial Data Handling(SDH'98)[C], Vancouver, BC, Canada, 1998, pp.45-54.
[173] K.Koperski and J.Han, Data Mining Methods for the Analysis of Large Geographic Databases[A], Proc. 10th Annual Conference on GIS[C], Vancouver, Canada, 1996.
[174] Knorr, E., R. Ng, Finding Aggregate Proximity Relationships and Commonalities in Spatial Data Mining[J], IEEE Trans. on Knowledge & Data Engineering, 8(6), pp.55-69, 1996.
[175] K. Wang, Y.He and J. Han, Mining Frequent Itemsets Using Support Constraints [A], Proc. 2000 Int. Conf. on Very Large Data Bases (VLDB'00)[C], Cairo, Egypt, 2000.
[176] Manfred Fischer, Henk J.Scholten and David Unwin(eds.), Spatial Analytical Perspectives on GIS[M], Taylor & Francis Ltd, 1996.
[177] M. Kamber, L. Winstone, W. Gong, S. Cheng, and J. Han, Generalization and Decision Tree Induction: Efficient Classification in Data Mining[A], Proc. of 1997 Int'l Workshop on Research Issues on Data Engineering (RIDE'97)[C], Birmingham, England, 1997, pp.111-120.
[178] M. Kamber, K. Koperski, G. Liu, Y.Lu, N.Stefanovic, L. Winstone, B. Xia, O. R. Zaiane, S.Zhang, H.Zhu, DBMiner: A System for Data Mining in Relational Databases and Data Warehouses[A], Proc. CASCON'97: Meeting of Minds[C], Toronto, Canada, 1997.
[179] M. S. Chen, J. Han, and P.S.Yu, Data Mining: An Overview from a Database Perspective[J], IEEE Transactions on Knowledge and Data Engineering, 8(6), 1996,pp.866-883.
[180] Nebojsa Stefanovic, J. Han, and K. Koperski, Object-Based Selective Materialization for Efficient Implementation of Spatial Data Cubes [J], IEEE Transactions on Knowledge and Data Engineering, 12(6), 2000, pp.938-958.
[181] Nebojsa Stefanovic, Design and Implementation of On-Line Analytical Processing (OLAP) of Spatial Data[D], M. Sc. thesis, Computing Science, Simon Fraser University, September 1997.
[182] O. R. Zaiane, J. Han, Z.N.Li, J.Y. Chiang, and S. Chee, MultiMedia-Miner: A System Prototype for MultiMedia Data Mining[A], Proc. 1998 ACM-SIGMOD Conf. on Management of Data[C], Seattle, Washington, 1998, pp.581-583.
[183] O. R.Zaiane, and J.Han, Resource and Knowledge Discovery in Global Information Systems: A Preliminary Design and Experiment[A], Proc. 1st Int'l Conf. on Knowledge Discovery and Data Mining(KDD'95)[C], Montreal, Canada, 1995, pp.331-336.
[184] R.Ng and J.Han, CLARANS: A Method for Clustering Objects for Spatial Data Mining[J], IEEE Trans. Knowledge & Data Engineering, 2001.
[185] R.Ng and J.Han, Efficient and Effective Clustering Method for Spatial Data Mining[A], Proc. of 1994 Int'l Conf. on Very Large Data Bases (VLDB'94) [C], Santiago, Chile, 1994, pp.144-155.
[186] R.Ng, L.V.S. Lakshmanan, J.Han and A.Pang, Exploratory Mining and Pruning Optimizations of Constrained Associations Rules[A], Proc. of 1998 ACM-SIGMOD Conf. on Management of Data, Seattle[C], Washington, June 1998, pp.13-24.
[187] Runying Mao, Adaptive-FP: An Efficient and Effective Method for Multi-level and Multi-dimensional Frequent Pattern Mining [D], M. Sc. thesis, Computing Science, Simon Fraser University, Apr. 2001.
[188] Shan Cheng, Statistical Approaches to Predictive Modeling in Large Databases [D], M. Sc.thesis, Computing Science, Simon Fraser University, March 1998.
[189] Shupeng Chen, Chenghu Zhou. Geo-graphic Information Science and Digital Earth[A]. Proc. of the International Symposium on Digital Earth[C], science Press, 1999.
[190] Shuyong Chen, chunsheng Xiao, Yanyou Qiao. Spatial Decision Support System and its General platform[A]. Proc. of the International Symposium on Distal Earth[C], science Press, 1999.
[191] Edited by Usama Fayyed, Georges G.Grinstein, Andreas Wierse. Information Visualization in Data Mining and Knowledge Discovery[C], Published in ACADEMIC PRESS, 2002.
[192] Tianxiang Yue, Chenghu Zhou. An Approach of Differential Geometry. to Data Mining[A]. Proceeding
of the International Symposium on Digital Earth[C], science Press, 1999.
[193] Wan Gong, Periodic Pattern Search in Time-Related Data Sets[D], M. Sc. thesis, Computing Science, Simon Fraser University, December 1997.
[194] W. Li, J. Han, and J. Pei, CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules[A], Proc. 2001 Int. Conf. on Data Mining(ICDM'01)[C], San Jose, CA, 2001.
[195] W. Lu, J. Han and B.C.Ooi, Discovery of General Knowledge in Large Spatial Databases[A], Proc. of 1993 Far East Workshop on Geographic Information Systems (FEGIS'93)[C], Singapore, June 1993, pp.275-289.
[196] X.Zhou, D. Truffet, and J.Han, Efficient Polygon Amalgamation Methods for Spatial OLAP and Spatial Data Mining [A], Proc. 6th Int. Symp. on Large Spatial Databases (SSD'99)[C], Hong Kong, July 1999, pp.167-187.
[197] Yin Jenny (Chiang) Tam, Datacube: Its Implementation and Application in OLAP Mining [D], M. Sc.thesis, Computing Science, Simon Fraser University, 1998.
[198] Yongiian Fu, Discovery of Multiple-Level R ules from Large Databases[D], Ph.D. thesis, Computing Science, Simon Fraser University, 1996.
[199] Zhang T, Ramakrishnam R, and Livny M.1997. BIRCH: A New Data Clustering Method and Its Applications[J]. Data Mining and Kaowledge Discovery, 1, pp. 141-182.