基于空间数据挖掘的MCSs时空演变规律研究
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摘要
随着空间信息获取技术的进步,现代数据采集技术得到了飞速的发展,不同类型的与气象有关的空间数据库以及数据库中的数据量正在快速增长,但大量的、有用的信息也被淹没在这些数据之中,没有得到充分的开发和利用。如何从这些海量数据中提取与灾害天气有关的模式、模型,进而成为预测灾害天气发生的信息和知识,是当前遥感学、地理信息系统、气象学家研究的前沿、重点和热点。
但由于气象数据的多数据源、多类型、多时相、多分辨率等固有的特点,指望一个数据挖掘系统挖掘所有类型的气象数据是不现实的。因此,为挖掘特定类型的数据,应当构造特定的数据挖掘系统。本文针对气象数据类型的多样性、复杂性,以及空间数据挖掘在气象中的应用存在的技术问题,以空间数据挖掘技术在青藏高原中尺度对流系统(MCSs)时空演变规律研究为背景,研究解决了如下问题:
1.如何从气象卫星红外遥感影像中定量化及客观地提取MCSs空间特征,构造特殊的MCSs时空数据库。
2.如何构造MCSs空间数据挖掘数据库
3.寻求有效的数据库约简方法
4. MCSs时空演变规则挖掘及其知识发现的可视化表达
本文将空间数据挖掘技术应用于青藏高原MCSs时空演变规律研究,对MCSs的移动和传播这一迄今的难题提供了研究思路和方法,其挖掘结果对预报高原MCSs东移影响长江中下游地区的强降水也很有帮助。
With the advancement of spatial information acquisition technology, modern data acquisition technology develops rapidly. Different types of spatial database and the data quantity that is relative with weather is increasing fast, but large amount of useful information is submerged in these data and can not got the best exploration and use. How to pick up the mode and model that is relative to disaster weather from these mass data and take them as the information and knowledge to forecast the happening of disaster weather become the researching emphasis, foreland and hotspot of remote sensing, geographical information system and meteorology.
But due to the inherent characteristics of the multi data source, multi type, multi temporal and multi resolution, one data mining system to excavate all types of weather data is unrealistic. So, in order to excavate specific types of data, specific data mining system should be constructed. Aiming at the diversity of weather data types and the technique problems existing in the application of spatial data mining, this thesis has a research and resolves the following problems, with the background of the research of Mesoscale Convective Systems (MCSs) Spatio-temporal evolution rules in Tibetan Plateau using spatial data mining technology.
1. How to extract the spatial features of MCSs in fit quantity and impersonal from the weather satellite infrared remote sensing image and construct specific MCSs spatial database?
2. How to construct MCSs spatial data mining database.
3. Seek efficient database reduction methods.
4. MCSs spatial evolution rules mining and its visual expression of knowledge discovery.
The data mining on MCSs in this thesis provides the research thought and methods for the study on moving and propagation of MCSs. The mining result is also helpful to forecast heavy rainfall in Yangtze River Basin with forecasting MCSs moving out to east from Tibetan Plateau.
但由于气象数据的多数据源、多类型、多时相、多分辨率等固有的特点,指望一个数据挖掘系统挖掘所有类型的气象数据是不现实的。因此,为挖掘特定类型的数据,应当构造特定的数据挖掘系统。本文针对气象数据类型的多样性、复杂性,以及空间数据挖掘在气象中的应用存在的技术问题,以空间数据挖掘技术在青藏高原中尺度对流系统(MCSs)时空演变规律研究为背景,研究解决了如下问题:
1.如何从气象卫星红外遥感影像中定量化及客观地提取MCSs空间特征,构造特殊的MCSs时空数据库。
2.如何构造MCSs空间数据挖掘数据库
3.寻求有效的数据库约简方法
4. MCSs时空演变规则挖掘及其知识发现的可视化表达
本文将空间数据挖掘技术应用于青藏高原MCSs时空演变规律研究,对MCSs的移动和传播这一迄今的难题提供了研究思路和方法,其挖掘结果对预报高原MCSs东移影响长江中下游地区的强降水也很有帮助。
With the advancement of spatial information acquisition technology, modern data acquisition technology develops rapidly. Different types of spatial database and the data quantity that is relative with weather is increasing fast, but large amount of useful information is submerged in these data and can not got the best exploration and use. How to pick up the mode and model that is relative to disaster weather from these mass data and take them as the information and knowledge to forecast the happening of disaster weather become the researching emphasis, foreland and hotspot of remote sensing, geographical information system and meteorology.
But due to the inherent characteristics of the multi data source, multi type, multi temporal and multi resolution, one data mining system to excavate all types of weather data is unrealistic. So, in order to excavate specific types of data, specific data mining system should be constructed. Aiming at the diversity of weather data types and the technique problems existing in the application of spatial data mining, this thesis has a research and resolves the following problems, with the background of the research of Mesoscale Convective Systems (MCSs) Spatio-temporal evolution rules in Tibetan Plateau using spatial data mining technology.
1. How to extract the spatial features of MCSs in fit quantity and impersonal from the weather satellite infrared remote sensing image and construct specific MCSs spatial database?
2. How to construct MCSs spatial data mining database.
3. Seek efficient database reduction methods.
4. MCSs spatial evolution rules mining and its visual expression of knowledge discovery.
The data mining on MCSs in this thesis provides the research thought and methods for the study on moving and propagation of MCSs. The mining result is also helpful to forecast heavy rainfall in Yangtze River Basin with forecasting MCSs moving out to east from Tibetan Plateau.
引文
1.蔡伟杰,张晚辉,朱建秋等.关联规则挖掘综述[J].计算机工程,2001,27(5):31-33
2.陈莉.数据库中的知识发现[J].西北大学学报.1999,29(1):5-7
3.陈雄南.高等数学(测绘专业用)[M],测绘出版社,北京,1978,12:359-392
4.陈渭民,肖稳安.卫星云图分析图册[M].南京:南京气象学院大气物理系,1988.12
5.陈述彭.数字地球百问[M].科学出版社,1999, 179
6.陈富赞,寇继凇,王以直.数据挖掘方法的研究[J].系统工程与电子技术,2000,22(8):78-81
7.陈国萍,李巍,刘仲英.数据挖掘中概念树的标准生成和实现[J].计算机工程, 2000,26(12):107-109
8.陈玉泉,朱锡钧,陆汝占.文本数据的数据挖掘算法[J].上海文通大学学报,2000,34(7):936-938
9.陈福集.数据挖掘在Intenet信息导航系统中的应用研究[J].电信科学,2000,9:14-16
10.邸凯昌.空间数据发掘与知识发现[M].武汉大学出版社,2000
11.丁夷.数据挖掘——技术与应用综述[M].西安邮电学院学报,1999.4
12.董春,张清浦,张家庆等.地理因子厍的建立及应用探讨[J].遥感信息2000,1:12-16
13.董超华主编.气象卫星业务产品释用手册[M].北京:气象出版社,1999.4
14.段晓君,杜小勇,易东云.可视化数据挖掘技术及其应用[J].计算机应用,2000,20(1):54-56
15.杜明义,郭达志.空间数据仓库技术与模型研究[J].计算机工程与应用,1999,12:16-18
16.杜明义,吉寿松,郭达志.基于空间数据仓库的数据采掘[J].计算机工程与应用,2000,11:32-34
17.方兆宝,林珲,吴立新等.流形群运动目标自动识别与跟踪技术研究[J].遥感学报,2004,8(1):14-22
18.方兆宝、林珲、吴立新等.流形群运动目标自动识别与跟踪模型参数的最优配置[J],武汉大学学报·信息科学版,2004,29(3):269-273
19.方兆宝,吴立新,林珲等.面向空间数据挖掘的中尺度对流系统演变规律研究[C]. GIS及遥感技术在水文水资源及环境中的应用国际会议,中国宜昌,2003.9
20.方兆宝,林珲,吴立新等.基于改良的面积重叠法自动跟踪MCSs[C]. GIS及遥感技术在水文水资源及环境中的应用国际会议,中国宜昌,2003.9
21.方兆宝,林珲,吴立新等.面向空间数据挖掘的MCSs移动和传播影响因素相关分析[J].热带气象学报,近期发表。
22.方兆宝,林珲,吴立新等. MCSs移出青藏高原规律的可视化表达与知识发现[J].解放军信息工程大学·测绘学院学报,2004,21(1):61-65
23.方依兰,黄智兴,张为群.股票信息的数据挖掘[J].西南师范大学学报(自然科学版),2000,25(2):138-142
24.高毅龙.数据挖掘及其工程诊断中的应用[D].西安交通大学博上学位论文,2000
25.关海英,阮秋琦.基于Hausdorff距离的非刚体目标自适应轮廓跟踪[J].通信学报,1998,19(11):38-43
26.国家气象局卫星气象中心.风云一号气象卫星图集[M].北京:气象出版社,1990.6
27.过仲阳,林珲,方兆宝等.卫星遥感揭示的青藏高原上MCS活动特征及移动传播.遥感学报.2003,7(5):350-357
28.过仲阳,林珲,方兆宝等.青藏高原上中尺度对流系统东移传播成因.地理信息科学. 2003.3,(1):5-10
29.郭达志,盛业华,杜培军等.地理信息系统原理与应用[M].中国矿业大学出版社,2002.12
30.郭达志,盛业华,余兆平等.地理信息系统基础与应用[M].煤炭工业出版社,1998.6
31.何强,何英. Matlab扩展编程[M].北京:清华大学出版社,2002.6
32.韩客松,王永成.文本挖掘、数据挖掘和知识管理——二十一世纪的智能信息处理[J].情报学报,2001,20(1):100-104
33.纪兆辉.数据挖掘技术初探[J].淮海工学院学报,1999,8(3):1-3
34.吉根林,帅克,孙志挥.数据挖掘技术及其应用[J].南京师大学报(自然科学版),2000,23(2):25-27
35.江吉喜,叶惠明.我国南方地区中-α尺度对流云团的研究[J],中国气象科学研究院院刊,1986,1(2):132-141
36.江吉喜、范梅珠.夏季青藏高原上的对流云和中尺度对流系统[J].大气科学, 2002,26(2): 263-270
37.李玉兰,王婧熔,郑新江等.我国西南-华南地区中尺度对流复合体(MCC)的研究[J],大气科学,1989, 13(4):417-422
38.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与方法[J].武汉:武汉大学学报(信息科学版),2002,3:221-233
39.李绍华. OLAP和数据挖掘技术在WEB日志上的应用[J].现代计算机,1999,72:16-18
40.李雄飞,苑森淼,懂立岩.基于相联规则的数据挖掘理论[J].吉林工业大学自然科学学报,2000,30(2):43-46
41.李业丽,常桂然,徐茜.神经网络在数据挖掘中的应用研究[J].计算机工程与应用,2000,8:103-105
42.刘宇,曲波,朱仲英,施颂椒.空间数据挖掘理论与方法的研究[J].微型电脑应用. 2000,16(8):15-18
43.廖志伟,孙雅明.数据挖掘技术及其在电力系统中的应用[J].电力系统自动化,2001,25(11):62-66
44.廖里,余英泽,吴渝等.数据挖掘和数据仓库及其在电信业中的应用[J].重庆邮电学院学报,2000,12(4):31-34
45.吕安民,林宗坚,李成名.数据挖掘和知识发现的技术方法[J].测绘科学,2000,25(4):36-39
46.罗云启、罗毅.数字化地理信息系统Mapinfo应用大全[M].北京:北京希望电子出版社,2001
47.毛广莉,黄梓龙,罗昌隆.电信网告警数据库中的数据挖掘[J].计算机应用研究,2000,8:98-99
48.师春香,江吉喜,方宗义. 1998长江大水期间对流云团活动特征研究[J],气候与环境研究,2000,
5(3):279-286
49.单寅,林珲,江吉喜等.夏季青藏高原上中尺度对流系统初生阶段特征.The 22nd Asian Conference on Remote Sensing ,5~9 Nov.2001,Singpore ?,2001 CRISP ,SISV,AARS:418-421
50.沈庭芝,方子文.数字图像处理及模式识别[M],北京理工大学出版社,北京,2000.1. 1-3,125-147
51.孙建华,赵思雄.华南“94·6”特大暴雨的中尺度对流系统及其环境场研究.大气科学, 2002,26(4):541-556
52.数学手册编写组.数学手册[M].北京:高等教育出版社,1979.5
53.孙家摒,舒宁,关泽群.遥感原理、方法和应用[M].测绘出版社,1999.10
54.田金兰,张素琴,黄刚.用关联规则方法挖掘保险业务数据中的投资风险规则.清华大学学报(自然科学版),2001,41(1):45-48
55.陶待言,张庆云,张顺利. 1998年长江流域洪涝灾害的气候背景和大尺度环流条件.气候与环境研究,1998,3(4):290-299
56.王国印,张赤红译,Namir clement shammas著. Visual c++使用指南[M].清华大学出版社,1997.6
57.王珊.数据仓库技术与联机分机处理[M],科学出版社,1998
58.王能斌.数据库系统原理[M],电子工业出版社,2000
59.王登炎. MCS的形态特征和外推预报[J].气象,2000,26(8):22-24
60.王冰清,怀进鹏. IDSS中数据仓库和数据挖掘的研究与实现[J].北京航空航天大学学报,1998,24(4):399-402
61.王立琨,陶祖钰,杨阳等. 1998年长江洪水大暴雨的卫星云图分析[J].北京大学学报, 2000, 36(1):87-94
62.黄荣辉,徐予红,王鹏飞等. 1998年夏长江流域特大洪涝特征及其成因探讨[J].气候与环境研究.1998,3(4):300-313
63.黄文骞.数字地图符号的形状描述与识别[J].测绘学报,1999,28(3):232-238
64.吴立新,史文中.地理信息系统远离预算法[M].科学出版社,2003.10
65.项续康,江吉喜.我国南方地区的中尺度对流复合体[J],应用气象学报,1995,6(11):9-17
66.闲侃,夏绍维.基于大型数据仓库的数据采掘.研究综述.软件学报,1998,9(1):53—63
67.徐强,何小荣,陈丙珍. FCC油品质量指标智能监测系统的数据挖掘与修正技术.计算机与应用化学,2000,17(3):210-214
68.夏云庆. Visual c++6.0数据库高级编程[M].北京希望电子出版社[M],2002.4
69.修文群,池天河.城市地理信息系统(GIS)[M].北京希望电子出版社,1999.6
70.杨正洪,郑齐健. SQL Server 7关系数据库系统管理与开发指南[M].机械工业出版社,2000.4
71.杨伦标,高英仪.模糊数学原理及应用(第二版)[M],华南理工大学出版社,广州,2000.3:59-64
72.杨会志.数据挖掘技术的主要方法及其发展方向[J].河北科技大学学报,2000,21(3):77-80
73.杨武,陈庄.数据库知识发现技术及应用[J].重庆工学院学报,2001,15(2):32-34
74.仪清菊,徐样德.不同尺度云团系统上下游的传播与1998年长江流域大暴雨[J].气候和环境研究,2001, 6(2):139-145
75.于秀林,任雪松.多元统计分析.中国统计出版社. 2003.3
76.赵思雄,孙建华,陈红等. 1998年7月长江流域特大洪水期间暴雨特征的分析研究.气候与环境研究,1998,3(4):368—381
77.赵建敏,郭庆. Visual Basic 6.0编程指南[M].航空工业出版社.2000.1
78.张顺利,陶诗言. 1998年夏季中国暴雨洪涝灾害的气象水文特征.应用气象学报.2001,012(004): 442-457
79.张顺利,陶诗言,张庆云等.长江中下游致洪暴雨的多尺度条件,科学通报,2002,47(6):467-473
80.张庆云,陶诗言,张顺利. 1998年嫩江松花江流域持续性暴雨的大尺度环流条件.大气科学,2001,25(4):567-576
81.张维明.数据仓库原理与应用[M].电子工业出版社.2002年3月.北京
82.章孝灿,黄智才,赵元洪.遥感数字图像处理[M].浙江大学出版社,1997:287-291
83.周成虎,张健挺.基于信息熵的地学空间数据挖掘模型[J].中国图象图形学报,1999,4(14):946-951
84.周斌,刘亚萍,吴泉谭.一个面向电子商务的数据挖掘系统的设计与实现.计算机工程,2000,26(6):18-20
85.祝国瑞,徐肇忠.普通地图制图中的数学方法[M].北京:测绘出版社,1990.6
86.朱天翔,李力,许占文.分类特征规则的数据挖掘技术[J].沈阳工业大学学报,1999,21(6):522-524
87. Arnaud, Yves, Michel Desbois, Joel Maizi, Automatic Tracking and characterization of African Convective Systems on Meteosat Pictures. Journal of Applied Meteorology, 1992,31(5):443–453
88. ALTONI G.A real-time video tracking system[J], IEEE trans PAMI, January, 1980,2(1):47-56
89. Abraham T, Roddick J F. Opportunities for knowledge discovery in spatio-temporal information systems [J]. Australian Journal of Information System, 1998, 5(2): 3-12
90. A.Feeldersa, H.Daniels, M.Holsheimer. Methodological and practical aspects of data mining. Information & Management,2000(37):271-281
91. Aggarwal C C, Philip S Y. Redefining clustering for high-dimensional applications [J]. IEEE Transactions on Knowledge and Data Engineering, 2002,14(2): 210-225
92. A. Goodman,Knowledge Discovery and the interface of Computing and Statistics. SIGKDD Explorations, Vol. l, Issue 1,June l999.Pp.12-20
93. Breiman L. Classification and regression trees[M]. Wadsworh,Belmont, 1984
94. B. D.Ripley. Pattern recognition and neural networks, Cambridge, UK: Cambridge university Press, 1996
95. C.Glymour, D.Madigan, D.Pregibon, and P. Smyth.Statistical Themes and Lessons for Data Mining.the 3rd Int. Conf. on Data Mining and Knowledge Discovery, 1997
96. D. Godberg. Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison-Wesley, 1989
97. D. P. Ballou, G.K. Tayi. Enhancing data quality in data warehouse environments[J]. Communications of ACM, 1999, 42:73-78
98. D.Bell,S.Anand and C.M.Shpcott. Data Mining in Spatial Database Int.Workshop on Spatial-Temporal Databases,1994
99. D.Cheung,J.Han,V.Ng, and Y.C.WonG.Maintenance of discovered association rules in large databases: An incremental updating technique. In Proc.1996 Int’1 Conf . on Data engineering. New Orleans, Louisiana, Feb.1996. Pp.106-114
100. David Hand, Heikki Mannila,Padhraic Smyth. Principles of Data Mining. 2001:272-276
101. Fang Zhaobao, Lin Hui, Wu Lixin, Jian Jixi, Huang Qiang. Studies on activities and evolvements of Mesoscale Convective Systems in summer of 1998 over Tibetan Plateau with spatial data mining oriented [C], the International Conference of GIS and RS in Hydrology, Water Resources and Environment (ICGRHWE), 16-19 September, 2003, China, Pp.422-423
102. Fang Zhaobao, Lin Hui, Wu Lixin, Jian Jixi, Huang Qiang. Automatically Tacking MCSs with Improved-Based Area-overlapped Method. [C], the International Conference of GIS and RS in Hydrology, Water Resources and Environment (ICGRHWE), 16-19 September, 2003, China, Pp.424-425
103. Frawley W, Piatetsk S G, Matheus C J. Knowledge discovery in databases: An Overview. in Knowledge Discovery in Databases [M]. MIT Press, 1991
104. Fayyad U, Weir N, and Djorgovski S. Automated cataloging and analysis of ski survey image databases: the SKICAT System [A]. In Proc. of the Second Int. Conf. on Information and Knowledge Management [C], Washington DC, 1993. Pp.527-536
105. G.Matheron,Random sets and integral geometry[M]. Wiley, 1975
106. G. Piatetsky-Shapiro. Guest Editor's Introduction: Knowledge Discovery in Databases from Research to Applications. Journal of Intelligent Information Systems (JIIS), 1995
107. Garreaud, R. D., and J. M. Wallace. The Diurnal March of Convective Cloudiness over the Americas. Mon. Wea. Rev., 1997,125: 3157–3171
108. Gore A., The Digital Earth: Understanding Our Plane in the 21st Century. The Lecture Note On The Science Center of California. 1998
109. Gold C.M. and Mostafavi M., Towards the Global GIS. ISPRS Journal of Photogrammetry and Remote Sensing, 2000, 55(3): 150-163
110. H. Mannila, Data mining machine learning statistics and databases. Proc. Of 8th International Conference on Scientific and Statistical Database Management. Stockholm,Sweden.1996.Pp.1-6
111. H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining[M]. Kluwer Academic Publishers, 1998
112. http://www.cse.unsw.edu.au/~quinlan/, 2003
113. http://www.celhk.com/it_calendar/it_courses/dw_wenig.html, 2003
114. Ishida T,Richard E.K. Moving-target search: a real-time search for changing goals[J]. IEEE trans. PAMI,June 1995,17(6): 609-619
115. IBM Intelligent Miner for Data. http://www-3.ibm.com.software.data.iminer,2003
116. J. Quinlan. Discovering rules by induction from large collections of examples, In Expert Systems in the micro Electronic Age., 1979.Pp.168-201
117. J.Serra, Image analysis and mathematical morphology[M]. Academic Press, 1982
118. J. Quinlan. Induction of decision trees, In Machine Learning, 1986. Pp.86-106
119. J. Quinlan and R. Rivest. Inferring decision trees using minimum description length principle, In Information and Computation, volume 80, 1989.Pp 227-248
120. J. R. Quinlan J. C4.5: programs for machine learning[M]. Morgan Kaufman, 1993
121. J,Sander, M.Easter, H.Kriegel and X.Xu. Density-Based Chustering in Spatial Databases: The Algorithm GDBSCAN and its Applications. Data Mining and Knowledge Discovery, 1998
122. J iawei Han, Micheline Kamber. Data Mining: Concepts and Techniques[M]. The Morgan Kaufmann Series in Data Management Systems, Jim Gray, Series Editor Morgan Kaufmann Publishers, August 2000. 550 pages. ISBN 1-55860-489-8
123. J.Han,K.Koperski,and N.Stefanovic.GeoMiner:A System Prototype for Spatial Data Mining.In Proc.1997 ACM-SIGMOD Int. Conf. on Management of Data(SIGMOD’97),Tucson,ARIZONA,May 1997. Pp.553-556
124. J. Han, Y. Fu, K.Koperski, G.Melli,W. Wang, O.R. Zaiane. Knowledge Mining in Databases. An Integration of Machine Leaning Methodologies with Database Technologies. Canadian Artificial Intelligence, October 1995
125. J.Han,Y.Cai and N.Cercone.Knowledge Discovery in Database:an Attribute Oriented Approach.Proc.of 18th VLDB,1992
126. J.Han, M.Kamber,and A.K.H.Tung. Spatial Clustering Methods in Data Mining: A Survey, H. Miller and J. Han (eds),Geographic Data Mining and Knowledge Discovery, Taylor and Francis,2001
127. J.Han, and Y. Fu. Discovery of Multiple-Level Association Rules from Large Databases. in Proc. 1995 VLDB, Zurich, Switzerland, Sept. 1995. Pp.420-431
128. K.Koperski, and J.Han.Discovery of Spatial Association Rules in Geographic information Databases. In Advances in Spatial Databases, Proceeding of 4th Symposium,SSD’95.Springer-Verlag,Berlin,1995,Pp.47-66
129. Kitamoto A. Data mining for typhoon image collection [A]. Proceedings of the 2nd International Workshop on Multimedia Data Mining [C], 2001. Pp.68-77
130. Kenneth R. Castleman. Digital Image Processing [M]. Prentice-Hall, Inc, 1996.3 389-400,412-420
131. K.Koperski and J.Han,Data Mining Methods for the Analysis of Large Geographic Databases, Proc. 10th Annual Conference on GIS, Vancouver, Canada, March 1996.Pp.88-94
132. K.Koperski, J.Adhihary and J.Han.Spatial Data Mining:Progress and Challenges.SIGMOD’96 Workshop on Research Issue On Data Mining and Knowledge Discovery(DMKD’96), Montreal,Canada,June,1996. Pp.333-336
133. K.Koperski and J.Han,et al. An efficient two-step method for classification of spatial data. In Proc. 8th Symp. Saptial Data Handling, Vancouver, Canada, 1998. Pp.45-55
134. Ke Wang, Senqiang Zhou, and He Yu. Growing decision trees on support-less association rules. In Proceedings of Knowledge Discovery and Data Mining (KDD), 2000
135. L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and regression trees[M]. Monterey, CA: Wadsworth International Group, 1984
136. Lee R S T, Liu J N K. An automatic satellite interpretation of tropical cyclone patterns using elastic graphdynamic link model [J]. IJPRAI, 1999, 13(8): 1251-1270
137. Lee R S T, Liu J N K. Atmosphere - Automatic Track Mining and Objective Satellite Pattern Hunting System Using Enhanced RBF and EGDLM [J]. AAAI/IAAI, 2000: 603-608
138. L. B. Holder. Intermediate decision trees[C]. In Proc. 14th Intl. Joint Conf. on Artificial Intelligence, , Montreal, Canada, Aug 1995.Pp.1056–1062
139. LEILA M. V. CARVALHO, CHARLES JONES. A Satellite Method to Identify Structural Properties of Mesoscale Convective Systems Based on the Maximum Spatial Correlation Tracking Technique (MASCOTTE)[J]. Journal of Applied Meteorology, 2001.10: 1683-1701
140. M.Estet,H.P.Kriegel,and X.XU. Knowledge discovery in large spatial database: Focusing techniques for efficient class identification. In Proc. 4th Int. Symp. Large Spatial Databases(SSD’95). Portland,ME, Aug.1995. Pp.67-82
141. M.Ester, H.Krieg el,J.Sander,and X.Xu. A Density-Based Algorithm for Discovering C1usters in Large Spatial Databases.In Proc. of the Second International Conference on Data Mining KDD-96, Portland ,Oregon,August 1996. Pp.226-231
142. M,S.Chen,J.Han and P.S.Yu.Data Mining: An 0verview form Database Perspective IEEEI transaction. Knowledge and Data Engineering, 1996. 8:866-883
143. M.Ester, A.Frommeit, H.Kriegel,and J.Sander. Algorithms for characterization and trend detection in spatial databases. In Proc. 1998 Int. Conf. Knowledge Discovery and Data Mining(KDD’98), New York,Aug.1998. Pp. 44-50
144. M.Ester, A.Frommeit, H.Kriegel,and J.Sander. Spatial Data Mining: Database Primitives,Algorithms and Efficient DBMS Support. Data Mining and Knowledge Discovery,2000
145. M.Ester, S.Gundlach,H.Kriegel,J.Sander. Database Primitives for Spatial Data Mining.Proc.Int Conf. on DaTABASES in Office,Engineering and Science,(BTW’99)、Ffreiburg, Germany , l999
146. Malerba D, Esposito F, Lisi F A. Mining spatial association rules in census data [A]. In Proc. 4th Int. Seminar on New Techniques and Technologies for Statistics (NTTS 2001) [C], Crete, 2001.Pp.18-22
147. P. Cheeseman, J. Kelly, and M. Self. Autoclass: a bayesian classification system, In Proceedings of 5th Int. Conf, on Machine Learning, Morgan Kaufmann, June 1988. Pp. 54-64
148. Quilan. C4.5 Release 8. http://www.cse.unsw.edu.au/quinlan/,2003
149. R.M.Haralick, L.G. Shapiro,Computer and robot vision[M]. .Addison-Wesley, 1993
150. R. Agrawal, S. Ghosh, T. Imielinski, B. Iyer and A. Swami, An interval classifier for database mining applications. In Proceedings of VLDB, 1992. Pp.560-573
151. R.T. Ng. & J.Han. Efficient and Effective Clustering Methods for Spatial Data Mining. Proc. of the 20th VLDB Conf., Santiago, Chile, 1994
152. R.Agrawal and R.Srikat. Fast Algorithms for Mining Association Rules in Large Database. Proc. of the 20th International Conference on Very Large Data Bases,Sept.1994.Pp.487-499
153. R.Srikant, and R.Agrawal. Mining Generalized Associalion Rules. In Proc. of the 21st VLDB Conf., Zurich, Switzerland, 1995
154. S. M. Weiss and I. Kapouleas. An empirical comparison of pattern recognition, neural nets, and machine learning classi-fication methods[C]. In Proc. 11th Intl. Joint Conf. on Artificial Intelligence, , Detroit, MI, Aug 1989. Morgan Kaufmann. Pp.781–787
155. Schuur, T. J., S. M. Hunter, W. D. Rust, and T. C. Marshall, Charge structure of a midlatitude mesoscale convective system. 16th Conference on Severe Local Storms, Kananaskis, Alberta, Canada, Amer. Meteor. Soc., 1990, Pp.663-668
156. S. Weiss and C. Kulikowski. Computer systems that learn: Classification and prediction methods fromstatistics, neural nets, machine learning and expert systems[M], San Mateo, CA: Morgan Kaufmann, 1991.
157. SAS Institute Inc. SAS Enterprise Miner. http://www.sas.com 2003
158. Shan Yin, Lin Hui, Ada Waichee Fu, Jiang Jixi, Huang Qian. The features of MCS over Tibetan Plateau[C]. the 22nd Asian Conference on Remote Sensing 5-9 November 2001, Singapore(c) 2001 CRISP, SISV, AARS,Pp.418-421
159. Surajit Chaudhuri, Umeshwar Dayal. An Overview of Data Warehousing and OLAP Technology[J]. SIGMOD Record, March, 1997,26(1): 26-30
160. T. Munakata. Knowledge Discovery. Communications of the ACM,November 1999,42(11):26-29
161. T.M.Mitchell.Machine Leaning and Data Miming. Communication of the ACM, November 1999,42(11):30-36
162. Tao S Y, Chen L. A review of recent research on the East Asian summer monsoon in China[J].In: Monsoon Meteorology. Oxford: Oxford University Press.1987: 60-92
163. U.M.Fayyad and R.Uthurusamy. editors. Proc.1st Int.Conf. Knowledge Discovery and Data Mining(KDD’95).Montreal,Canada,Aug.1995.AAAI Press.
164. U.M.Fayyad,G.Piatetsky-Shapiro,P.Smyth.From data mining to knowledge discovery: an overview. In Fayyad et a1.Eds.Advance in knowledge Discovery and Data Mining.Cambridge, MA: AAAI/MIT Press.1996
165. U.M.Fayydd,Gpiatetsky- Shapiro, and P. Smyth, and R.Uthurusamy, editors. Advances in Knowledge Discovery and Data Mining. Cambridge, MA:AAAI/MIT Pess. 1996
166. Wright D. and Goodchild M.F., Data from Deep: Implications for the GIS Community. International Journal of Geographical Information Science, 1997, 11(6): 523-528
167. W.Lu, J.Han,and B.C.Ooi. Discovery of General Knowledge in Large Spatial Databases.In Proc.Far East Workshop on Geographic information Systems. Singapore,June 1993. Pp.275-289
168. W.Li,J.Han,and J.Pei,CMAR:Accurate and Efficient C1assification Based on Multiple Class-Association Rules,Proc.2001 Int.Conf. on Data Mining(ICDM-01),San Jose,CA, Nov. 2001
169. Yu, C.-K, F. Roux, B. F. Smull, D.P. Jorgensen, and O. Bousquet, Airborne Doppler observations of a mountainous convective system during MAP IOP5. 30th Conference on Radar Meteorology, 19-24 July 2001, Munich, Germany, Amer. Meteor. Soc., 2001, pp.530-532
170. Y. L. Cheung. Mining interesting itemsets with item constraints. In Thesis: Techniques in Data Mining, Decision Tree Classification and Constraint-based Itemsets Mining. The Chinese University of Hong Kong. June, 2001. Pp 88-109
171. Zhou Z, Chen S, Chen Z. Mining typhoon knowledge with neural networks [A]. In Proc. 11th Int. Conf. on Tools with Artificial Intelligence [C], IEEE, 1999, pp.325–326
172. Zhu Qiangen, He Jinhai, Wang Panxing. A study of circulation difference between East-Asian and Indian summer monsoons with their interaction[J]. Advances in Atmospheric Sciences, 1986,3:446-477
173. J. Han, Y. Fu, K.Koperski, G.Melli,W. Wang, O.R. Zaiane.DMQL:A data mining query language for relational databases. In Proc. 1996 SIGMOD,96 Workshop Reseach Issues on Data Mining and Knowledge Discovery(DMKD’96), Montreal, Canada, June 1996. Pp.27-34
2.陈莉.数据库中的知识发现[J].西北大学学报.1999,29(1):5-7
3.陈雄南.高等数学(测绘专业用)[M],测绘出版社,北京,1978,12:359-392
4.陈渭民,肖稳安.卫星云图分析图册[M].南京:南京气象学院大气物理系,1988.12
5.陈述彭.数字地球百问[M].科学出版社,1999, 179
6.陈富赞,寇继凇,王以直.数据挖掘方法的研究[J].系统工程与电子技术,2000,22(8):78-81
7.陈国萍,李巍,刘仲英.数据挖掘中概念树的标准生成和实现[J].计算机工程, 2000,26(12):107-109
8.陈玉泉,朱锡钧,陆汝占.文本数据的数据挖掘算法[J].上海文通大学学报,2000,34(7):936-938
9.陈福集.数据挖掘在Intenet信息导航系统中的应用研究[J].电信科学,2000,9:14-16
10.邸凯昌.空间数据发掘与知识发现[M].武汉大学出版社,2000
11.丁夷.数据挖掘——技术与应用综述[M].西安邮电学院学报,1999.4
12.董春,张清浦,张家庆等.地理因子厍的建立及应用探讨[J].遥感信息2000,1:12-16
13.董超华主编.气象卫星业务产品释用手册[M].北京:气象出版社,1999.4
14.段晓君,杜小勇,易东云.可视化数据挖掘技术及其应用[J].计算机应用,2000,20(1):54-56
15.杜明义,郭达志.空间数据仓库技术与模型研究[J].计算机工程与应用,1999,12:16-18
16.杜明义,吉寿松,郭达志.基于空间数据仓库的数据采掘[J].计算机工程与应用,2000,11:32-34
17.方兆宝,林珲,吴立新等.流形群运动目标自动识别与跟踪技术研究[J].遥感学报,2004,8(1):14-22
18.方兆宝、林珲、吴立新等.流形群运动目标自动识别与跟踪模型参数的最优配置[J],武汉大学学报·信息科学版,2004,29(3):269-273
19.方兆宝,吴立新,林珲等.面向空间数据挖掘的中尺度对流系统演变规律研究[C]. GIS及遥感技术在水文水资源及环境中的应用国际会议,中国宜昌,2003.9
20.方兆宝,林珲,吴立新等.基于改良的面积重叠法自动跟踪MCSs[C]. GIS及遥感技术在水文水资源及环境中的应用国际会议,中国宜昌,2003.9
21.方兆宝,林珲,吴立新等.面向空间数据挖掘的MCSs移动和传播影响因素相关分析[J].热带气象学报,近期发表。
22.方兆宝,林珲,吴立新等. MCSs移出青藏高原规律的可视化表达与知识发现[J].解放军信息工程大学·测绘学院学报,2004,21(1):61-65
23.方依兰,黄智兴,张为群.股票信息的数据挖掘[J].西南师范大学学报(自然科学版),2000,25(2):138-142
24.高毅龙.数据挖掘及其工程诊断中的应用[D].西安交通大学博上学位论文,2000
25.关海英,阮秋琦.基于Hausdorff距离的非刚体目标自适应轮廓跟踪[J].通信学报,1998,19(11):38-43
26.国家气象局卫星气象中心.风云一号气象卫星图集[M].北京:气象出版社,1990.6
27.过仲阳,林珲,方兆宝等.卫星遥感揭示的青藏高原上MCS活动特征及移动传播.遥感学报.2003,7(5):350-357
28.过仲阳,林珲,方兆宝等.青藏高原上中尺度对流系统东移传播成因.地理信息科学. 2003.3,(1):5-10
29.郭达志,盛业华,杜培军等.地理信息系统原理与应用[M].中国矿业大学出版社,2002.12
30.郭达志,盛业华,余兆平等.地理信息系统基础与应用[M].煤炭工业出版社,1998.6
31.何强,何英. Matlab扩展编程[M].北京:清华大学出版社,2002.6
32.韩客松,王永成.文本挖掘、数据挖掘和知识管理——二十一世纪的智能信息处理[J].情报学报,2001,20(1):100-104
33.纪兆辉.数据挖掘技术初探[J].淮海工学院学报,1999,8(3):1-3
34.吉根林,帅克,孙志挥.数据挖掘技术及其应用[J].南京师大学报(自然科学版),2000,23(2):25-27
35.江吉喜,叶惠明.我国南方地区中-α尺度对流云团的研究[J],中国气象科学研究院院刊,1986,1(2):132-141
36.江吉喜、范梅珠.夏季青藏高原上的对流云和中尺度对流系统[J].大气科学, 2002,26(2): 263-270
37.李玉兰,王婧熔,郑新江等.我国西南-华南地区中尺度对流复合体(MCC)的研究[J],大气科学,1989, 13(4):417-422
38.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与方法[J].武汉:武汉大学学报(信息科学版),2002,3:221-233
39.李绍华. OLAP和数据挖掘技术在WEB日志上的应用[J].现代计算机,1999,72:16-18
40.李雄飞,苑森淼,懂立岩.基于相联规则的数据挖掘理论[J].吉林工业大学自然科学学报,2000,30(2):43-46
41.李业丽,常桂然,徐茜.神经网络在数据挖掘中的应用研究[J].计算机工程与应用,2000,8:103-105
42.刘宇,曲波,朱仲英,施颂椒.空间数据挖掘理论与方法的研究[J].微型电脑应用. 2000,16(8):15-18
43.廖志伟,孙雅明.数据挖掘技术及其在电力系统中的应用[J].电力系统自动化,2001,25(11):62-66
44.廖里,余英泽,吴渝等.数据挖掘和数据仓库及其在电信业中的应用[J].重庆邮电学院学报,2000,12(4):31-34
45.吕安民,林宗坚,李成名.数据挖掘和知识发现的技术方法[J].测绘科学,2000,25(4):36-39
46.罗云启、罗毅.数字化地理信息系统Mapinfo应用大全[M].北京:北京希望电子出版社,2001
47.毛广莉,黄梓龙,罗昌隆.电信网告警数据库中的数据挖掘[J].计算机应用研究,2000,8:98-99
48.师春香,江吉喜,方宗义. 1998长江大水期间对流云团活动特征研究[J],气候与环境研究,2000,
5(3):279-286
49.单寅,林珲,江吉喜等.夏季青藏高原上中尺度对流系统初生阶段特征.The 22nd Asian Conference on Remote Sensing ,5~9 Nov.2001,Singpore ?,2001 CRISP ,SISV,AARS:418-421
50.沈庭芝,方子文.数字图像处理及模式识别[M],北京理工大学出版社,北京,2000.1. 1-3,125-147
51.孙建华,赵思雄.华南“94·6”特大暴雨的中尺度对流系统及其环境场研究.大气科学, 2002,26(4):541-556
52.数学手册编写组.数学手册[M].北京:高等教育出版社,1979.5
53.孙家摒,舒宁,关泽群.遥感原理、方法和应用[M].测绘出版社,1999.10
54.田金兰,张素琴,黄刚.用关联规则方法挖掘保险业务数据中的投资风险规则.清华大学学报(自然科学版),2001,41(1):45-48
55.陶待言,张庆云,张顺利. 1998年长江流域洪涝灾害的气候背景和大尺度环流条件.气候与环境研究,1998,3(4):290-299
56.王国印,张赤红译,Namir clement shammas著. Visual c++使用指南[M].清华大学出版社,1997.6
57.王珊.数据仓库技术与联机分机处理[M],科学出版社,1998
58.王能斌.数据库系统原理[M],电子工业出版社,2000
59.王登炎. MCS的形态特征和外推预报[J].气象,2000,26(8):22-24
60.王冰清,怀进鹏. IDSS中数据仓库和数据挖掘的研究与实现[J].北京航空航天大学学报,1998,24(4):399-402
61.王立琨,陶祖钰,杨阳等. 1998年长江洪水大暴雨的卫星云图分析[J].北京大学学报, 2000, 36(1):87-94
62.黄荣辉,徐予红,王鹏飞等. 1998年夏长江流域特大洪涝特征及其成因探讨[J].气候与环境研究.1998,3(4):300-313
63.黄文骞.数字地图符号的形状描述与识别[J].测绘学报,1999,28(3):232-238
64.吴立新,史文中.地理信息系统远离预算法[M].科学出版社,2003.10
65.项续康,江吉喜.我国南方地区的中尺度对流复合体[J],应用气象学报,1995,6(11):9-17
66.闲侃,夏绍维.基于大型数据仓库的数据采掘.研究综述.软件学报,1998,9(1):53—63
67.徐强,何小荣,陈丙珍. FCC油品质量指标智能监测系统的数据挖掘与修正技术.计算机与应用化学,2000,17(3):210-214
68.夏云庆. Visual c++6.0数据库高级编程[M].北京希望电子出版社[M],2002.4
69.修文群,池天河.城市地理信息系统(GIS)[M].北京希望电子出版社,1999.6
70.杨正洪,郑齐健. SQL Server 7关系数据库系统管理与开发指南[M].机械工业出版社,2000.4
71.杨伦标,高英仪.模糊数学原理及应用(第二版)[M],华南理工大学出版社,广州,2000.3:59-64
72.杨会志.数据挖掘技术的主要方法及其发展方向[J].河北科技大学学报,2000,21(3):77-80
73.杨武,陈庄.数据库知识发现技术及应用[J].重庆工学院学报,2001,15(2):32-34
74.仪清菊,徐样德.不同尺度云团系统上下游的传播与1998年长江流域大暴雨[J].气候和环境研究,2001, 6(2):139-145
75.于秀林,任雪松.多元统计分析.中国统计出版社. 2003.3
76.赵思雄,孙建华,陈红等. 1998年7月长江流域特大洪水期间暴雨特征的分析研究.气候与环境研究,1998,3(4):368—381
77.赵建敏,郭庆. Visual Basic 6.0编程指南[M].航空工业出版社.2000.1
78.张顺利,陶诗言. 1998年夏季中国暴雨洪涝灾害的气象水文特征.应用气象学报.2001,012(004): 442-457
79.张顺利,陶诗言,张庆云等.长江中下游致洪暴雨的多尺度条件,科学通报,2002,47(6):467-473
80.张庆云,陶诗言,张顺利. 1998年嫩江松花江流域持续性暴雨的大尺度环流条件.大气科学,2001,25(4):567-576
81.张维明.数据仓库原理与应用[M].电子工业出版社.2002年3月.北京
82.章孝灿,黄智才,赵元洪.遥感数字图像处理[M].浙江大学出版社,1997:287-291
83.周成虎,张健挺.基于信息熵的地学空间数据挖掘模型[J].中国图象图形学报,1999,4(14):946-951
84.周斌,刘亚萍,吴泉谭.一个面向电子商务的数据挖掘系统的设计与实现.计算机工程,2000,26(6):18-20
85.祝国瑞,徐肇忠.普通地图制图中的数学方法[M].北京:测绘出版社,1990.6
86.朱天翔,李力,许占文.分类特征规则的数据挖掘技术[J].沈阳工业大学学报,1999,21(6):522-524
87. Arnaud, Yves, Michel Desbois, Joel Maizi, Automatic Tracking and characterization of African Convective Systems on Meteosat Pictures. Journal of Applied Meteorology, 1992,31(5):443–453
88. ALTONI G.A real-time video tracking system[J], IEEE trans PAMI, January, 1980,2(1):47-56
89. Abraham T, Roddick J F. Opportunities for knowledge discovery in spatio-temporal information systems [J]. Australian Journal of Information System, 1998, 5(2): 3-12
90. A.Feeldersa, H.Daniels, M.Holsheimer. Methodological and practical aspects of data mining. Information & Management,2000(37):271-281
91. Aggarwal C C, Philip S Y. Redefining clustering for high-dimensional applications [J]. IEEE Transactions on Knowledge and Data Engineering, 2002,14(2): 210-225
92. A. Goodman,Knowledge Discovery and the interface of Computing and Statistics. SIGKDD Explorations, Vol. l, Issue 1,June l999.Pp.12-20
93. Breiman L. Classification and regression trees[M]. Wadsworh,Belmont, 1984
94. B. D.Ripley. Pattern recognition and neural networks, Cambridge, UK: Cambridge university Press, 1996
95. C.Glymour, D.Madigan, D.Pregibon, and P. Smyth.Statistical Themes and Lessons for Data Mining.the 3rd Int. Conf. on Data Mining and Knowledge Discovery, 1997
96. D. Godberg. Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison-Wesley, 1989
97. D. P. Ballou, G.K. Tayi. Enhancing data quality in data warehouse environments[J]. Communications of ACM, 1999, 42:73-78
98. D.Bell,S.Anand and C.M.Shpcott. Data Mining in Spatial Database Int.Workshop on Spatial-Temporal Databases,1994
99. D.Cheung,J.Han,V.Ng, and Y.C.WonG.Maintenance of discovered association rules in large databases: An incremental updating technique. In Proc.1996 Int’1 Conf . on Data engineering. New Orleans, Louisiana, Feb.1996. Pp.106-114
100. David Hand, Heikki Mannila,Padhraic Smyth. Principles of Data Mining. 2001:272-276
101. Fang Zhaobao, Lin Hui, Wu Lixin, Jian Jixi, Huang Qiang. Studies on activities and evolvements of Mesoscale Convective Systems in summer of 1998 over Tibetan Plateau with spatial data mining oriented [C], the International Conference of GIS and RS in Hydrology, Water Resources and Environment (ICGRHWE), 16-19 September, 2003, China, Pp.422-423
102. Fang Zhaobao, Lin Hui, Wu Lixin, Jian Jixi, Huang Qiang. Automatically Tacking MCSs with Improved-Based Area-overlapped Method. [C], the International Conference of GIS and RS in Hydrology, Water Resources and Environment (ICGRHWE), 16-19 September, 2003, China, Pp.424-425
103. Frawley W, Piatetsk S G, Matheus C J. Knowledge discovery in databases: An Overview. in Knowledge Discovery in Databases [M]. MIT Press, 1991
104. Fayyad U, Weir N, and Djorgovski S. Automated cataloging and analysis of ski survey image databases: the SKICAT System [A]. In Proc. of the Second Int. Conf. on Information and Knowledge Management [C], Washington DC, 1993. Pp.527-536
105. G.Matheron,Random sets and integral geometry[M]. Wiley, 1975
106. G. Piatetsky-Shapiro. Guest Editor's Introduction: Knowledge Discovery in Databases from Research to Applications. Journal of Intelligent Information Systems (JIIS), 1995
107. Garreaud, R. D., and J. M. Wallace. The Diurnal March of Convective Cloudiness over the Americas. Mon. Wea. Rev., 1997,125: 3157–3171
108. Gore A., The Digital Earth: Understanding Our Plane in the 21st Century. The Lecture Note On The Science Center of California. 1998
109. Gold C.M. and Mostafavi M., Towards the Global GIS. ISPRS Journal of Photogrammetry and Remote Sensing, 2000, 55(3): 150-163
110. H. Mannila, Data mining machine learning statistics and databases. Proc. Of 8th International Conference on Scientific and Statistical Database Management. Stockholm,Sweden.1996.Pp.1-6
111. H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining[M]. Kluwer Academic Publishers, 1998
112. http://www.cse.unsw.edu.au/~quinlan/, 2003
113. http://www.celhk.com/it_calendar/it_courses/dw_wenig.html, 2003
114. Ishida T,Richard E.K. Moving-target search: a real-time search for changing goals[J]. IEEE trans. PAMI,June 1995,17(6): 609-619
115. IBM Intelligent Miner for Data. http://www-3.ibm.com.software.data.iminer,2003
116. J. Quinlan. Discovering rules by induction from large collections of examples, In Expert Systems in the micro Electronic Age., 1979.Pp.168-201
117. J.Serra, Image analysis and mathematical morphology[M]. Academic Press, 1982
118. J. Quinlan. Induction of decision trees, In Machine Learning, 1986. Pp.86-106
119. J. Quinlan and R. Rivest. Inferring decision trees using minimum description length principle, In Information and Computation, volume 80, 1989.Pp 227-248
120. J. R. Quinlan J. C4.5: programs for machine learning[M]. Morgan Kaufman, 1993
121. J,Sander, M.Easter, H.Kriegel and X.Xu. Density-Based Chustering in Spatial Databases: The Algorithm GDBSCAN and its Applications. Data Mining and Knowledge Discovery, 1998
122. J iawei Han, Micheline Kamber. Data Mining: Concepts and Techniques[M]. The Morgan Kaufmann Series in Data Management Systems, Jim Gray, Series Editor Morgan Kaufmann Publishers, August 2000. 550 pages. ISBN 1-55860-489-8
123. J.Han,K.Koperski,and N.Stefanovic.GeoMiner:A System Prototype for Spatial Data Mining.In Proc.1997 ACM-SIGMOD Int. Conf. on Management of Data(SIGMOD’97),Tucson,ARIZONA,May 1997. Pp.553-556
124. J. Han, Y. Fu, K.Koperski, G.Melli,W. Wang, O.R. Zaiane. Knowledge Mining in Databases. An Integration of Machine Leaning Methodologies with Database Technologies. Canadian Artificial Intelligence, October 1995
125. J.Han,Y.Cai and N.Cercone.Knowledge Discovery in Database:an Attribute Oriented Approach.Proc.of 18th VLDB,1992
126. J.Han, M.Kamber,and A.K.H.Tung. Spatial Clustering Methods in Data Mining: A Survey, H. Miller and J. Han (eds),Geographic Data Mining and Knowledge Discovery, Taylor and Francis,2001
127. J.Han, and Y. Fu. Discovery of Multiple-Level Association Rules from Large Databases. in Proc. 1995 VLDB, Zurich, Switzerland, Sept. 1995. Pp.420-431
128. K.Koperski, and J.Han.Discovery of Spatial Association Rules in Geographic information Databases. In Advances in Spatial Databases, Proceeding of 4th Symposium,SSD’95.Springer-Verlag,Berlin,1995,Pp.47-66
129. Kitamoto A. Data mining for typhoon image collection [A]. Proceedings of the 2nd International Workshop on Multimedia Data Mining [C], 2001. Pp.68-77
130. Kenneth R. Castleman. Digital Image Processing [M]. Prentice-Hall, Inc, 1996.3 389-400,412-420
131. K.Koperski and J.Han,Data Mining Methods for the Analysis of Large Geographic Databases, Proc. 10th Annual Conference on GIS, Vancouver, Canada, March 1996.Pp.88-94
132. K.Koperski, J.Adhihary and J.Han.Spatial Data Mining:Progress and Challenges.SIGMOD’96 Workshop on Research Issue On Data Mining and Knowledge Discovery(DMKD’96), Montreal,Canada,June,1996. Pp.333-336
133. K.Koperski and J.Han,et al. An efficient two-step method for classification of spatial data. In Proc. 8th Symp. Saptial Data Handling, Vancouver, Canada, 1998. Pp.45-55
134. Ke Wang, Senqiang Zhou, and He Yu. Growing decision trees on support-less association rules. In Proceedings of Knowledge Discovery and Data Mining (KDD), 2000
135. L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and regression trees[M]. Monterey, CA: Wadsworth International Group, 1984
136. Lee R S T, Liu J N K. An automatic satellite interpretation of tropical cyclone patterns using elastic graphdynamic link model [J]. IJPRAI, 1999, 13(8): 1251-1270
137. Lee R S T, Liu J N K. Atmosphere - Automatic Track Mining and Objective Satellite Pattern Hunting System Using Enhanced RBF and EGDLM [J]. AAAI/IAAI, 2000: 603-608
138. L. B. Holder. Intermediate decision trees[C]. In Proc. 14th Intl. Joint Conf. on Artificial Intelligence, , Montreal, Canada, Aug 1995.Pp.1056–1062
139. LEILA M. V. CARVALHO, CHARLES JONES. A Satellite Method to Identify Structural Properties of Mesoscale Convective Systems Based on the Maximum Spatial Correlation Tracking Technique (MASCOTTE)[J]. Journal of Applied Meteorology, 2001.10: 1683-1701
140. M.Estet,H.P.Kriegel,and X.XU. Knowledge discovery in large spatial database: Focusing techniques for efficient class identification. In Proc. 4th Int. Symp. Large Spatial Databases(SSD’95). Portland,ME, Aug.1995. Pp.67-82
141. M.Ester, H.Krieg el,J.Sander,and X.Xu. A Density-Based Algorithm for Discovering C1usters in Large Spatial Databases.In Proc. of the Second International Conference on Data Mining KDD-96, Portland ,Oregon,August 1996. Pp.226-231
142. M,S.Chen,J.Han and P.S.Yu.Data Mining: An 0verview form Database Perspective IEEEI transaction. Knowledge and Data Engineering, 1996. 8:866-883
143. M.Ester, A.Frommeit, H.Kriegel,and J.Sander. Algorithms for characterization and trend detection in spatial databases. In Proc. 1998 Int. Conf. Knowledge Discovery and Data Mining(KDD’98), New York,Aug.1998. Pp. 44-50
144. M.Ester, A.Frommeit, H.Kriegel,and J.Sander. Spatial Data Mining: Database Primitives,Algorithms and Efficient DBMS Support. Data Mining and Knowledge Discovery,2000
145. M.Ester, S.Gundlach,H.Kriegel,J.Sander. Database Primitives for Spatial Data Mining.Proc.Int Conf. on DaTABASES in Office,Engineering and Science,(BTW’99)、Ffreiburg, Germany , l999
146. Malerba D, Esposito F, Lisi F A. Mining spatial association rules in census data [A]. In Proc. 4th Int. Seminar on New Techniques and Technologies for Statistics (NTTS 2001) [C], Crete, 2001.Pp.18-22
147. P. Cheeseman, J. Kelly, and M. Self. Autoclass: a bayesian classification system, In Proceedings of 5th Int. Conf, on Machine Learning, Morgan Kaufmann, June 1988. Pp. 54-64
148. Quilan. C4.5 Release 8. http://www.cse.unsw.edu.au/quinlan/,2003
149. R.M.Haralick, L.G. Shapiro,Computer and robot vision[M]. .Addison-Wesley, 1993
150. R. Agrawal, S. Ghosh, T. Imielinski, B. Iyer and A. Swami, An interval classifier for database mining applications. In Proceedings of VLDB, 1992. Pp.560-573
151. R.T. Ng. & J.Han. Efficient and Effective Clustering Methods for Spatial Data Mining. Proc. of the 20th VLDB Conf., Santiago, Chile, 1994
152. R.Agrawal and R.Srikat. Fast Algorithms for Mining Association Rules in Large Database. Proc. of the 20th International Conference on Very Large Data Bases,Sept.1994.Pp.487-499
153. R.Srikant, and R.Agrawal. Mining Generalized Associalion Rules. In Proc. of the 21st VLDB Conf., Zurich, Switzerland, 1995
154. S. M. Weiss and I. Kapouleas. An empirical comparison of pattern recognition, neural nets, and machine learning classi-fication methods[C]. In Proc. 11th Intl. Joint Conf. on Artificial Intelligence, , Detroit, MI, Aug 1989. Morgan Kaufmann. Pp.781–787
155. Schuur, T. J., S. M. Hunter, W. D. Rust, and T. C. Marshall, Charge structure of a midlatitude mesoscale convective system. 16th Conference on Severe Local Storms, Kananaskis, Alberta, Canada, Amer. Meteor. Soc., 1990, Pp.663-668
156. S. Weiss and C. Kulikowski. Computer systems that learn: Classification and prediction methods fromstatistics, neural nets, machine learning and expert systems[M], San Mateo, CA: Morgan Kaufmann, 1991.
157. SAS Institute Inc. SAS Enterprise Miner. http://www.sas.com 2003
158. Shan Yin, Lin Hui, Ada Waichee Fu, Jiang Jixi, Huang Qian. The features of MCS over Tibetan Plateau[C]. the 22nd Asian Conference on Remote Sensing 5-9 November 2001, Singapore(c) 2001 CRISP, SISV, AARS,Pp.418-421
159. Surajit Chaudhuri, Umeshwar Dayal. An Overview of Data Warehousing and OLAP Technology[J]. SIGMOD Record, March, 1997,26(1): 26-30
160. T. Munakata. Knowledge Discovery. Communications of the ACM,November 1999,42(11):26-29
161. T.M.Mitchell.Machine Leaning and Data Miming. Communication of the ACM, November 1999,42(11):30-36
162. Tao S Y, Chen L. A review of recent research on the East Asian summer monsoon in China[J].In: Monsoon Meteorology. Oxford: Oxford University Press.1987: 60-92
163. U.M.Fayyad and R.Uthurusamy. editors. Proc.1st Int.Conf. Knowledge Discovery and Data Mining(KDD’95).Montreal,Canada,Aug.1995.AAAI Press.
164. U.M.Fayyad,G.Piatetsky-Shapiro,P.Smyth.From data mining to knowledge discovery: an overview. In Fayyad et a1.Eds.Advance in knowledge Discovery and Data Mining.Cambridge, MA: AAAI/MIT Press.1996
165. U.M.Fayydd,Gpiatetsky- Shapiro, and P. Smyth, and R.Uthurusamy, editors. Advances in Knowledge Discovery and Data Mining. Cambridge, MA:AAAI/MIT Pess. 1996
166. Wright D. and Goodchild M.F., Data from Deep: Implications for the GIS Community. International Journal of Geographical Information Science, 1997, 11(6): 523-528
167. W.Lu, J.Han,and B.C.Ooi. Discovery of General Knowledge in Large Spatial Databases.In Proc.Far East Workshop on Geographic information Systems. Singapore,June 1993. Pp.275-289
168. W.Li,J.Han,and J.Pei,CMAR:Accurate and Efficient C1assification Based on Multiple Class-Association Rules,Proc.2001 Int.Conf. on Data Mining(ICDM-01),San Jose,CA, Nov. 2001
169. Yu, C.-K, F. Roux, B. F. Smull, D.P. Jorgensen, and O. Bousquet, Airborne Doppler observations of a mountainous convective system during MAP IOP5. 30th Conference on Radar Meteorology, 19-24 July 2001, Munich, Germany, Amer. Meteor. Soc., 2001, pp.530-532
170. Y. L. Cheung. Mining interesting itemsets with item constraints. In Thesis: Techniques in Data Mining, Decision Tree Classification and Constraint-based Itemsets Mining. The Chinese University of Hong Kong. June, 2001. Pp 88-109
171. Zhou Z, Chen S, Chen Z. Mining typhoon knowledge with neural networks [A]. In Proc. 11th Int. Conf. on Tools with Artificial Intelligence [C], IEEE, 1999, pp.325–326
172. Zhu Qiangen, He Jinhai, Wang Panxing. A study of circulation difference between East-Asian and Indian summer monsoons with their interaction[J]. Advances in Atmospheric Sciences, 1986,3:446-477
173. J. Han, Y. Fu, K.Koperski, G.Melli,W. Wang, O.R. Zaiane.DMQL:A data mining query language for relational databases. In Proc. 1996 SIGMOD,96 Workshop Reseach Issues on Data Mining and Knowledge Discovery(DMKD’96), Montreal, Canada, June 1996. Pp.27-34