面向精准农业的空间数据挖掘技术研究与应用
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摘要
随着“3S”技术在农业领域的不断普及,农业数据增长迅速,农业已成为空间数据挖掘最富有机遇与挑战性的应用领域之一。本文是在实施国家“863”项目“玉米精准作业系统研究与应用”的过程中,基于土壤肥力数据库和玉米精准作业的要求,利用空间数据挖掘技术,提出了解决玉米精准施肥、土壤肥力评价、地力等级分类和产量预测等问题的新方法,研究成果已成功应用于玉米精准作业智能决策系统中。主要工作和创新点:
1.进行了基于空间模糊聚类算法的玉米精准施肥的研究。使用模糊聚类分析方法,建立土壤养分分类模型;利用八连通法进行空间聚类分析,并将模糊聚类结果应用于空间聚类。这种两阶段聚类方法优于传统的单阶段聚类,其分类结果对玉米精准施肥具有重要的指导意义。
2.提出了基于加权的空间模糊动态聚类算法及在土壤肥力评价中的应用。该算法与基于模糊等价关系的传递闭包方法进行比较表明,其聚类准确率要明显高于未加权的模糊聚类算法。将其改进的算法运用到精准农业的土壤肥力评价中,与实际情况相符。
3.研究了基于粗糙集-决策树的优化算法及在地力评价中的应用。研究结果表明基于聚类的样本优选方法去除了大量冗余样本,基于粗糙集的属性约简方法去除了部分冗余属性,使用决策树方法构建决策树,节省了时间和空间,降低了模型的复杂度。因而,本文提出的聚类和粗糙集约简相结合的方法在时间、空间和准确性方面均优于其他方法,该算法能有效提高土壤地力等级分类的准确性和客观性。
4.采用时间序列算法中的滑动求和自回归方法(ARIMA)来对玉米产量进行预测,实验结果表明应用ARIMA模型预测的玉米产量与实际值拟合效果很好。
5.设计并实现了玉米精准作业智能空间决策支持系统(MPISDSS)。该系统将具有空间信息处理功能的地理信息系统、具有空间信息分析功能的空间数据挖掘技术、人工智能领域中的专家系统技术与传统的信息管理系统、决策支持系统有效集成,并将GIS中的统计分析方法与数据可视化结合起来,极大提高了农业管理部门进行农业生产决策的能力。
Spatial data mining is the emerging multi-disciplinary cross-edge disciplines with the development of computer technology database applications and management decision support technology. With the growing popularity of 3S technology in agricultural and the rapid growth of agricultural data agricultural Information Science has become one of the richest opportunities and challenges of applications of the spatial data mining. This article discusses several key issues of spatial data mining and intelligent spatial decision-making that corn precision operation intelligent decision support system involved the process of the implementation of the national 863 project of corn precise operating system research and applications and presents a space-based data mining of the corn precision operation intelligent spatial decision support system and from the theory and practice of the system to explore research and application and carries out exploratory research and application from the theory and practice. Based on requests of fertility database and corn precision operation using of spatial data mining this paper presents a new method to solve soil fertility corn precise fertilization soil fertility level and yield forecasting. The research result has been successfully applied to maize precise operating intelligent decision-making system. Major work and innovation points in the following areas:
1. For the Spatial cluster research present situation of spatial clustering and the actual needs of precision agriculture intelligent decision-making in order to solve regional problems of corn precision fertilizer corn precision fertilization based on spatial fuzzy clustering algorithm has been researched. Using fuzzy clustering analysis method to study the classification of soil nutrient studies and establish classification model using 8-connected to carry on spatial clustering analysis and the results of fuzzy clustering applied to spatial clustering. This two-stage clustering method is superior to the traditional single-stage clustering. It has some significance for spatial data mining and theoretical studies. The main contribution of this approach is that :(1) Spatial fuzzy clustering method for the first time applies to soil nutrient content analysis; (2) The existing clustering algorithms are simple non-spatial attribute clustering or spatial location based on nearby clustering but for precision agriculture in the precise fertilization must also consider the attributes and spatial location combination of the two clustering algorithms have not been reported previously. This classification is in line with the needs of corn precise fertilization. The classification has an important guiding significance for corn precision fertilizer and other field management and operation of precision agriculture to do decision making so make precision agriculture toward a more practical direction.
2. Spatial fuzzy clustering algorithm based on weighted has been proposed application of weighted spatial fuzzy dynamic clustering algorithm can be able to conduct more accurate evaluation of soil fertility. For variable rate fertilization on soil nutrient study on the impact common practice is to compare soil N P K and other nutrients and do not comprehensive consideration of various nutrients in the soil with each other. There is also a part of the study of variable rate fertilization effects on soil nutrients. This paper presents an improved spatial fuzzy dynamic clustering algorithm and its application in soil fertility evaluation in view of traditional spatial fuzzy clustering algorithm insufficiency.
The specific process including: first obtains various attribute weight using the analytic hierarchy process then unifies the weight and the spatial fuzzy dynamic cluster law in the end use F distribution in probability statistics to determine the best classification to improve the spatial fuzzy clustering algorithm for intelligent. Compared the method of the paper with the transitive closure method based on fuzzy equivalence relation.
The experimental results show that: the algorithm clustering accuracy rate is significantly higher than the weighted fuzzy clustering algorithm. The improved algorithm is applied to soil fertility evaluation of precision agriculture the experimental results is in line with the actual situation. It makes a contribution to the accurate evaluation of soil fertility.
3. Research on the optimization algorithm based on rough set– decision tree and application on soil fertility evaluation. Looking from data mining's angle the soil evaluation materially belongs to the classified forecast question. Decision tree method is a better classification method suitable for dealing with nonlinear data and classification and predicted decision-making problems of describe data the speed of production decision tree is quick easy to extract the rules easy to understand. When the attributes in data set are excessively more some situations is easy to be appear when classifies with the decision tree such as decision tree structure is not good discover some useful rules information is difficulty and so on.
In the rough set theory knowledge is interpreted as a variety of division on the domain model using two precision approximation and lower approximation sets to approximate the collection to be described is suitable for dealing with uncertain knowledge. Rough set theory can support data mining and knowledge discovery in multiple steps such as data preprocessing data reduction rule generation data acquisition and other dependencies. So rough sets and decision tree have a strong advantage of complementarities. This article uses the clustering method to obtain samples and the samples is more typical checked by decision tree model so the samples can be used as a standard model of learning samples; using rough set attribute reduction method combined with decision tree method to evaluate the productivity grade of soil that is use the rough set method to reduce the land attributes and obtain a low-dimensional training data then use decision tree method to build decision tree obtain classified rule sets. This method not only reduces the tree's branches removing redundant attributes improve mining efficiency; and also enhance the model accuracy. The algorithm can effectively improve the soil fertility level classification accuracy and objectivity. And provides new ideas and methods for spatial data mining and knowledge discovery.
4. Use of time series algorithm to predict corn yield has been studied corn production forecast is an important part of the implementation process of precision agriculture the traditional statistical methods are relatively large errors the cost of production forecast application of remote sensing maps is relatively high. In this paper time series algorithm method of sliding summation autoregressive (ARIMA) is used to predict the yield of maize and makes up for the shortcomings of statistical methods big errors but also remotes sensing measurements to verify the results produced. Experimental results show that the predict corn yield application of ARIMA models is very good with the actual value fitting effect and indicate that time series algorithm can be used on maize production to better predict future trends. This has provided the new mentality and the method for the corn rate of prediction analysis.
5. Corn precise assignment is a complex spatial decision-making tasks this article Uses artificial intelligence techniques spatial analysis methods statistical analysis methods digital maps image analysis and visualization methods and so on in-depth analysis space factors time factors that accurate operations variables affecting corn fertilizing spraying variables intelligent measuring production that impact on variable rate fertilization variable spraying intelligent measuring production in corn precision operation and design and implement the maize precise intelligent space decision-making system.
The system will effectively integrate geographic information system with spatial information processing functions spatial data mining techniques with spatial information analysis capabilities expert system technology in the field of artificial intelligence traditional information management systems Decision Support System and combine the statistical analysis of GIS methods and data visualization thus enhancing the capacity that agricultural administrative department full use of various properties and spatial information data to make decisions.
The actual application shows that the system is very suitable for precision agriculture intelligent decision-making and further enriched and perfected the practice-oriented field study of spatial data mining techniques and methods.
1.进行了基于空间模糊聚类算法的玉米精准施肥的研究。使用模糊聚类分析方法,建立土壤养分分类模型;利用八连通法进行空间聚类分析,并将模糊聚类结果应用于空间聚类。这种两阶段聚类方法优于传统的单阶段聚类,其分类结果对玉米精准施肥具有重要的指导意义。
2.提出了基于加权的空间模糊动态聚类算法及在土壤肥力评价中的应用。该算法与基于模糊等价关系的传递闭包方法进行比较表明,其聚类准确率要明显高于未加权的模糊聚类算法。将其改进的算法运用到精准农业的土壤肥力评价中,与实际情况相符。
3.研究了基于粗糙集-决策树的优化算法及在地力评价中的应用。研究结果表明基于聚类的样本优选方法去除了大量冗余样本,基于粗糙集的属性约简方法去除了部分冗余属性,使用决策树方法构建决策树,节省了时间和空间,降低了模型的复杂度。因而,本文提出的聚类和粗糙集约简相结合的方法在时间、空间和准确性方面均优于其他方法,该算法能有效提高土壤地力等级分类的准确性和客观性。
4.采用时间序列算法中的滑动求和自回归方法(ARIMA)来对玉米产量进行预测,实验结果表明应用ARIMA模型预测的玉米产量与实际值拟合效果很好。
5.设计并实现了玉米精准作业智能空间决策支持系统(MPISDSS)。该系统将具有空间信息处理功能的地理信息系统、具有空间信息分析功能的空间数据挖掘技术、人工智能领域中的专家系统技术与传统的信息管理系统、决策支持系统有效集成,并将GIS中的统计分析方法与数据可视化结合起来,极大提高了农业管理部门进行农业生产决策的能力。
Spatial data mining is the emerging multi-disciplinary cross-edge disciplines with the development of computer technology database applications and management decision support technology. With the growing popularity of 3S technology in agricultural and the rapid growth of agricultural data agricultural Information Science has become one of the richest opportunities and challenges of applications of the spatial data mining. This article discusses several key issues of spatial data mining and intelligent spatial decision-making that corn precision operation intelligent decision support system involved the process of the implementation of the national 863 project of corn precise operating system research and applications and presents a space-based data mining of the corn precision operation intelligent spatial decision support system and from the theory and practice of the system to explore research and application and carries out exploratory research and application from the theory and practice. Based on requests of fertility database and corn precision operation using of spatial data mining this paper presents a new method to solve soil fertility corn precise fertilization soil fertility level and yield forecasting. The research result has been successfully applied to maize precise operating intelligent decision-making system. Major work and innovation points in the following areas:
1. For the Spatial cluster research present situation of spatial clustering and the actual needs of precision agriculture intelligent decision-making in order to solve regional problems of corn precision fertilizer corn precision fertilization based on spatial fuzzy clustering algorithm has been researched. Using fuzzy clustering analysis method to study the classification of soil nutrient studies and establish classification model using 8-connected to carry on spatial clustering analysis and the results of fuzzy clustering applied to spatial clustering. This two-stage clustering method is superior to the traditional single-stage clustering. It has some significance for spatial data mining and theoretical studies. The main contribution of this approach is that :(1) Spatial fuzzy clustering method for the first time applies to soil nutrient content analysis; (2) The existing clustering algorithms are simple non-spatial attribute clustering or spatial location based on nearby clustering but for precision agriculture in the precise fertilization must also consider the attributes and spatial location combination of the two clustering algorithms have not been reported previously. This classification is in line with the needs of corn precise fertilization. The classification has an important guiding significance for corn precision fertilizer and other field management and operation of precision agriculture to do decision making so make precision agriculture toward a more practical direction.
2. Spatial fuzzy clustering algorithm based on weighted has been proposed application of weighted spatial fuzzy dynamic clustering algorithm can be able to conduct more accurate evaluation of soil fertility. For variable rate fertilization on soil nutrient study on the impact common practice is to compare soil N P K and other nutrients and do not comprehensive consideration of various nutrients in the soil with each other. There is also a part of the study of variable rate fertilization effects on soil nutrients. This paper presents an improved spatial fuzzy dynamic clustering algorithm and its application in soil fertility evaluation in view of traditional spatial fuzzy clustering algorithm insufficiency.
The specific process including: first obtains various attribute weight using the analytic hierarchy process then unifies the weight and the spatial fuzzy dynamic cluster law in the end use F distribution in probability statistics to determine the best classification to improve the spatial fuzzy clustering algorithm for intelligent. Compared the method of the paper with the transitive closure method based on fuzzy equivalence relation.
The experimental results show that: the algorithm clustering accuracy rate is significantly higher than the weighted fuzzy clustering algorithm. The improved algorithm is applied to soil fertility evaluation of precision agriculture the experimental results is in line with the actual situation. It makes a contribution to the accurate evaluation of soil fertility.
3. Research on the optimization algorithm based on rough set– decision tree and application on soil fertility evaluation. Looking from data mining's angle the soil evaluation materially belongs to the classified forecast question. Decision tree method is a better classification method suitable for dealing with nonlinear data and classification and predicted decision-making problems of describe data the speed of production decision tree is quick easy to extract the rules easy to understand. When the attributes in data set are excessively more some situations is easy to be appear when classifies with the decision tree such as decision tree structure is not good discover some useful rules information is difficulty and so on.
In the rough set theory knowledge is interpreted as a variety of division on the domain model using two precision approximation and lower approximation sets to approximate the collection to be described is suitable for dealing with uncertain knowledge. Rough set theory can support data mining and knowledge discovery in multiple steps such as data preprocessing data reduction rule generation data acquisition and other dependencies. So rough sets and decision tree have a strong advantage of complementarities. This article uses the clustering method to obtain samples and the samples is more typical checked by decision tree model so the samples can be used as a standard model of learning samples; using rough set attribute reduction method combined with decision tree method to evaluate the productivity grade of soil that is use the rough set method to reduce the land attributes and obtain a low-dimensional training data then use decision tree method to build decision tree obtain classified rule sets. This method not only reduces the tree's branches removing redundant attributes improve mining efficiency; and also enhance the model accuracy. The algorithm can effectively improve the soil fertility level classification accuracy and objectivity. And provides new ideas and methods for spatial data mining and knowledge discovery.
4. Use of time series algorithm to predict corn yield has been studied corn production forecast is an important part of the implementation process of precision agriculture the traditional statistical methods are relatively large errors the cost of production forecast application of remote sensing maps is relatively high. In this paper time series algorithm method of sliding summation autoregressive (ARIMA) is used to predict the yield of maize and makes up for the shortcomings of statistical methods big errors but also remotes sensing measurements to verify the results produced. Experimental results show that the predict corn yield application of ARIMA models is very good with the actual value fitting effect and indicate that time series algorithm can be used on maize production to better predict future trends. This has provided the new mentality and the method for the corn rate of prediction analysis.
5. Corn precise assignment is a complex spatial decision-making tasks this article Uses artificial intelligence techniques spatial analysis methods statistical analysis methods digital maps image analysis and visualization methods and so on in-depth analysis space factors time factors that accurate operations variables affecting corn fertilizing spraying variables intelligent measuring production that impact on variable rate fertilization variable spraying intelligent measuring production in corn precision operation and design and implement the maize precise intelligent space decision-making system.
The system will effectively integrate geographic information system with spatial information processing functions spatial data mining techniques with spatial information analysis capabilities expert system technology in the field of artificial intelligence traditional information management systems Decision Support System and combine the statistical analysis of GIS methods and data visualization thus enhancing the capacity that agricultural administrative department full use of various properties and spatial information data to make decisions.
The actual application shows that the system is very suitable for precision agriculture intelligent decision-making and further enriched and perfected the practice-oriented field study of spatial data mining techniques and methods.
引文
[1]王天真.智能融合数据挖掘算法及其应用[D].上海:上海海事大学,2006.
[2]苗玉彬.精准农业智能测产系统的研究与应用[D].上海:上海交通大学,2003.
[3]冯红伟.数据挖掘技术的研究与应用[D].西安:西北工业大学,2002.6
[4]李德仁,关泽群空间信息系统的集成与实现[M].武汉:武汉大学出版社,2000.
[5]周海燕.空间数据挖掘的研究[D],北京:中国人民解放军信息工程大学,2003.4
[6]龙腾芳.数据挖掘技术在农业领域中的应用研究[J],微计算机信息,2005,35(13):40-53.
[7]栾家辉.车载GPS测产系统入校正装置[D],哈尔滨:东北农业大学,2003.1.
[8]郑良永,曹启民,夏炜林,胡剑非.精准农业发展趋势及其在我国的应用[J].华南热带农业大学学报,2005.01.
[9] Ng R T,Han J,Efficient and Effective Clustering Methods for Spatial Data Mining[C].In The 20th Very Large Databases Conference Santiago Chile.1994.
[10]Li D R,Cheng T. KDG Knowledge Discovery from GIS[C].In Proceedings of the Canadian Conference on GIS.Ottawa,1994.
[11]胡彩平,秦小麟.空间数据挖掘研究综述[J].计算机科学技术,2007,34(5):14-19.
[12] Shashi S,Chawla S.空间数据库[M].谢昆青,马修军,杨冬青等译.北京:机械工业出版社.2004
[13]李永森.SDMKD及智能空间决策支持系统研究[D],安徽:合肥工业大学,2006.7.
[14]樊明辉.空间数据挖掘及其可视化系统若干关键技术研究[D].中国科学院,2006.6.
[15]涂建东,陈崇成,樊明辉等.基于Java3D的空间关联规则可视化原理与实现[J].高技术通讯.2004(6):98-99.
[16] Ng R,Han J .Efficient and effective clustering method for spatial data mining Porc1994[J]. Int Conf Very Large Data bases(VLDB’94).1994:144-155.
[17] EsterMartinHnasPeter,KriegelJorg,SanderXiaowei Xu.A DensityBased Algorithm for Discovering Clusters in Lagre Spatial Daatbases with Noise Proceedings of the 2nd Intenrational Conferernce Knowledge Discovey and Data Mining(KDD96)Portland OregonAugust 1996.
[18]李德仁,王树良,李德毅等.空间数据挖掘理论与应用[M].北京:科学出版社,2006.
[19]韩家炜.数据挖掘:概念与技术[M].北京:机械工业出版社,2007.6.
[20]张雪伍,苏奋振,石忆邵等.空间关联规则挖掘研究进展[J].地理科学进展,2007,11:122-125.
[21]Robert Bembenik,Henryk Rybiski.Mining Spatial Association Rules with No Distance Parameter Intelligent Information Processing and Web Mining[C].Springer Berlin/Heidelberg.2006,35:499-508.
[22]M Ester, H P Kriegel,J Sander. Spatial data mining:A database approach[J].In Advances in Spatial Databases, 1997,1262:47-66.
[23]刘大有,王生生,虞强源等.基于定性空间推理的多层空间关联规则挖掘算法[J].计算机研究与发展.2004,41(4):565-570.
[24]LAN Rongqing,LIU Zengliang,YANG Xiaomei.Methods of Mining Fuzzy Spatial Association Rules[J].Journal of Institute of Surveying and Mapping.2005,22(1):36-39.
[25]Beaubouef Theresa,Petry Frederick E.A rough set foundation for spatial data mining involving vague regions[C].IEEE International Conference on Plasma Science. 2002,1:767-772.
[26]Beaubouef Theresa,Ladner Roy,Petry Frederick.Rough Set Spatial Data Modeling for Data Mining International[J].Journal of Intelligent Systems Granular Computing andData Mining.2004,19(7):567-584.
[27]Seo EunKyoung,Biggerstaff Michael I.Impact of cloud model microphysics on passive microwave retrievals of cloud properties Part II Uncertainty in rain hydrometeor structure and latent heating retrievals[J].Journal of Applied Meteorology and Climatology.2006,45(7):955-972.
[28]Yang Bin Zhu,ZhongYing.Mining multilevel spatial association rules with cloud models[J].Journal of Harbin Institute of Technology (New Series).2005,12(3):314-318.
[29]Wang Xiao Hui,Xie Jiancang,Li Jianxun et al.Implementation of GIS spatial data mining based on cloud theory[C].Proceedings 2006 International Conference on Hybrid Information Technology ICHIT 2006.2006,1:530-534.
[30]L K Sharma,O P Vyas,U S Tiwary et al.A Novel Approach of Multilevel Positive and Negative Association Rule Mining for Spatial Databases Machine Learning and Data Mining in Pattern Recognition[C].Springer Berlin/Heidelberg.2005,3(587):620-629.
[31]William Perrizo,Qin Ding,Qiang Ding et al.Deriving High Confidence Rules from Spatial Data Using Peano Count TreesIn Proc Advances in WebAge Information Management[C].Springer Berlin/Heidelberg.2001,2(118):91-102.
[32]吴习宇.粗糙集理论在农业决策支持系统知识发现中的应用[D].西南农业大学.2005.
[33]Aijun AnNing,ShanChristine,ChanNick et al.Discovering Rules for Water Demand Prediction[C]. An Enhanced Rough Set Approach Artificial Inetlligence.1996, 9(6):645-653.
[34]GrzymalaBusse.JWLERSA System of Knowledge Discovery Based on Rough[C]SetsIn Proc of 5th Intl Workship RSFD 96Tokyo.1996:443-444.
[35]梁本哲.基于粗糙集理论的土地利用规划决策支持系统模型及其应用研究[D].中国地质大学.2007.
[36]熊新惠.粗糙集理论在银行数据分析中的利用[D].安徽大学.2005.
[37]梁西燕.基于粗糙集理论的农业决策支持系统研究[D].西北农林科技大学.2008.
[38]郭红波,周明全,耿国华,王小凤.粗糙集理论分析及其在农业数据中的应用[J].计算机技术与发展.2007(10):165-167,188.
[39]朱绍文等.决策树采掘技术及发展趋势[J].计算机工程.2002,28(8):77-78.
[40]史忠植.知识发现[M].北京:清华大学出版社,2002,12.
[41]成文丽.基于决策树的数据挖掘算法的技术研究[D].太原理工大学.2003.
[42]Quinlan J R.C4.5 Programs for Machine Learning[M].Morgan Kaufmann.1992.
[43]R Groth.数据挖掘构筑企业竞争优势[M].侯迪等译.西安:西安交通大学出版社,2001:146-186.
[44]Fayyad U M et al.1996 Advances in Knowledge Discovery and Data Mining Menlopark CA AAAI/MIT Press Frasconi P Gori M Soda G 1999 Data categorization using decision trellises[C].IEEE Transactions on Knowledge and Data Engineering. 1996,11(5):697-712.
[45]Marsala C,Bigolin N M. Spatial data mining with fuzzy decision trees[J].In Data Mining edited by Ebecken N F F (Ashurst Lodge; UK WIT Press/Computational Mechanics Publications).1998:235-248.
[46]Cantu Paz, E Kmaath C.Indueing oblique decision trees with evolutionary Algorithms[J] IEEE Transaetions on Evolutionary Computation.2003,7(l):54~68.
[47]Stone P,Veloso M.Using decision tree confidence factors formultiagent control[C]. Minnesota,USA Proeeedings of the 2nd International Conference on Autonomous Agents.1998.
[48]Koperski K. A Progressive Refinement Approach to Spatial Data Mining[D] Ph D Thesis British Columbia Simon Fraser University. 1999.
[49]孙微微,胡月明,刘才兴等.基于决策树的土壤质量等级研究[J]华南农业大学学报(自然科学版) 2005.03.
[50]薛正平,邓华,杨星卫等.基于决策树和图层叠置的精准农业产量图分析方法[J].农业工程学报,Vol 22,No 8,2006,8.140-144.
[51]王占全.基于地理信息系统空间数据挖掘若干关键技术的研究[D].淅江大学,2005.
[52]芮小平.空间信息可视化关键技术研究——以25维、三维、多维可视化为例[D].中国科学院.2004.
[53]Ankerst M.et al.1999.3D shape histograms for similarity search and classification in spatial database[C].Lecture Notes in Computer Science 1651;207-225.
[54]Maceachren A M.et al. Constructing knowledge from multivariate spatiotemporal dataintegrating geographical visualization with knowledge discovery in database methods International[J]. Joural of Geographical Information Science 1999,13(4):311-334.
[55]Michael T. Rosenstein,Paul R. Cohen,Concepts from time series, Proc. Of the fifteenth national/tenth Conference on Artificial intelligence/Innovative apPlications of artificial intelligence,Madison,Wisconsin,Untied States,July 1998.739-745.
[56]F.Tkens. Detecting strange attractors in turbulence,Proc.of Dynamical Systems and Turbulence,Waruick,1980.366-381.
[57]Yoon J P,Lee J,Kim S. Trend Similarity and perdiction in time-series database[A]. Proc of SPIE on Data Mining and Knowledge Discovery:Theory Tools,and Technology II[C]. Washington:SPIE,2000.201-212.
[58]G. Das, K.Lin, H.Mannila, G.Renganathan, P.Smyth. Rule Discovery form time series, Proceedings of the Fourth International Conference on Knowledge Diseovery and Data Mining(KDD-98), AAAIPress, 1998.16-22.
[59]欧阳为民,蔡庆生.数据库中的时态数据挖掘研究[J].计算机科学.1998,25(4):60-63.
[60]刘念祖.时态数据挖掘的探讨[J].上海第二工业大学学报. 2001(2):27-31.
[61]张保稳.时间序列数据挖掘研究[D].西北工业大学.2002.
[62]Dempster A P, Laird N M ,Rubin D B. Maximum likelihood from incomplete data via the EM algorithm[J]. Journal of Royal Statistical Society ,1977 , B(39) :1-38.
[63]Ambroise C,Govaert G. Convergence of an EMtype algorithm for spatial clustering Pattern[C].Recognition Letters 1998,19(10):919-927.
[64]张瑞菊,陶华学.基于模糊神经网络的空间数据挖掘方法研究[J].山东科技大学学报(自然科学版),2003,22(4):24-27.
[65]Piatetsky-shapiro G.,1994. An overview of knowledge discovery in databases:recent progress and challenges In Rough Sets,Fuzzy Sets and Knowledge Discovery[C]. edited by Wojciech P. Niarko.Berlin:Spring-Verlag,1-10.
[66]王树良.基于数据场与模型的空间数据挖掘和知识发现[D].武汉大学,2002.
[67]李雄飞,李军等编著.数据挖掘与知识发现[M].北京:高等教育出版社,2003.
[68]汪杭军.基于粗糙集的空间数据挖掘研究[D].贵州大学,2006.
[69]张建军.GPS与GIS技术在ITS系统中的应用研究[D].长安大学,2004.
[70]Clark PNiblet TT.The CN2 induction algorithm[J].Machine Leaning.1989(3):261-283.
[71]杨春成.空间数据挖掘中聚类分析算法的研究[D].解放军信息工程大学,2004.
[72]符晓蓉.空间数据挖掘不确定性的研究[D].武汉理工大学,2007.
[73]WANG Shengsheng,LIU Dayou,CAO Bin,LIU Jie.A subspace clustering algorithm for high dimensional spatial data[J].Computer Applications.2005,11.2615-2617.
[74]Koperski K,Han J.Discovery of Spatial Association Rules in Geographic Information Databases[C].In Proceedings of the 4th International Symposium on Large Spatial Databases(SSD95) Maine,1995,647-667.
[75]Koperski K,Adhikary J, Han J. Spatial Data Mining : Progress and Challenges. In SIGMOD’96 Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD’96)[C], Mont real , Canada ,1996.
[76]Ng R,Han J.Efficient and effective clustering method for spatial data mining[C].Porc 1994 Int Conf Very Large Data bases(VLDB’94).1994,144-155.
[77]Wang Lizhen,Xie Kunqing,Chen Tao,et al. Efficient discovery of multilevel spatial association rules using partitions[J].Information and Software Technology.2005, 4(7):829-840.
[78]刘君强,潘云鹤.挖掘空间关联规则的前缀树算法设计与实现[J].中国图象图形学报.2003,8(4):476-480.
[79]Shashi S,Yan Huang.Discovering spatial colocation patterns:Asummary of results[A].In Proceedings of the 7th International Symposium on Spatial and Temporal Databases[C].Redondo Beach,CA,2001,23-62.
[80]Clementini E,Felice P.D.Koperski K. Mining Multiple level Spatial Association Rules for Objects with a Broad Boundary[J]. Data and Knowledge Engineering,2000,34:251-270.
[81]Kacar E,Cicekli N.K.Discovery Fuzzy Spatial Association Rules[C].Data Mining and Knowledge Discovery:Theory,Tools and Technology IV. In Dasarathy B V,ed.Proceedings of SPIE, Vol4730,2002,94-102.
[82]Zadeh LA.Fuzzy Sets[J].Information and Control.1965,8(3):338-353.
[83]张宏宇.粗糙集理论及其在数据挖掘中的应用研究[D].山西大学,2003.
[84]曹付元.多值信息系统与决策表知识约简[D].山西大学,2004.
[85]张文修,吴伟志,梁吉业,李德玉.粗糙集理论与方法[M].北京:科学出版社,2001.
[86]高小康.粗糙集理论研究及其在工程和医学诊断中的应用[D].同济大学,2007.
[87]Ziarko W.Variable Precision Rough Set Model[J].Journal of Computer and Sciences.1993,46(1):39-59.
[88]Kryszkiewicz M.Rough set approach to incomplete information systems[J].Information Sciences.1998,11(2):39-49.
[89]陈文伟编著.决策支持系统教程[M].北京:清华大学出版社.2006.7.
[90]成文丽.基于决策树的数据挖掘算法的技术研究[D].太原理工大学,2003.
[91]黄晓芳.数据挖掘中决策树算法及其应用[J].兵工自动化,2005,(02):35-36.
[92]王振龙.时间序列分析[M].北京:中国统计出版社,2000.2.
[93]InmonW.H. Buiding the Data Warahouse(Second Edition )[M].New York:John Wiley & Sons,1996.
[94]Srivastsva J.,Cheng P.Y.warahouse creation-a potential roadblock to data warahousing[J].IEEE Transactions on Knowledge and Data Engineering.1999,11(1):118-126.
[95]史文中,王树良.GIS中属性不确定性的处理方法及其发展[J].遥感学报,2002.5.
[96]李琦,杨超伟.空间数据仓库及其构建策略[J].中国图象图形学报,1999,4[A](11):984-990.
[97]杨群,闾国年,陈钟明.地理信息数据仓库的技术研究[J].中国图象图形学报,1999,4[A](8):621-626.
[98]Zhou X.,Truffet D.,Han J.Efficient polygon amalgamation methods for spatial OLAP and spatial data mining[C].Lecture Notes in Computer Science,1999,16(51):167-187.
[99]Yao Y. Y. ,Wong S. K. M. , Lin T. Y. A Review of Rough Set Models[C].In Rough Sets and Data Minning Anakysis for Imprecise Data, edited by Y. LIN and N.CERCONE. London:Kluwer.Academic Publishers,1997,47-75.
[100]扈立家,唐雪漫.辽宁省构建精准农业技术体系问题研究[J].辽宁行政学院学报,2006年3期.
[101]Jiawei Han,Micheline Kamber.Data Mining Concept and Techniques[M].China Machine Press.2005,223-232.
[102]ZHU Qiang,WANG Ye.Fuzzy Clustering Analysis and Its Application in Data Mining[J].Modern Computer.2007,4:87-88.
[103]Shengsheng WANG,Dayou LIU, Xinying WANG, Jie LIU. Spatial Reasoning Based Spatial Data Mining for Precision Agriculture[C]. H.T. Shen et al. (Eds.):APWeb Workshops 2006, LNCS 3842.SpringerVerlag Berlin Heidelberg.2006,506–510.
[104]Xu R..Survey of clustering algorithms[C].IEEE Trans. Neu. Netw.2005,16(3):645-678.
[105]罗兰星.模糊聚类分析中传递闭包法及其应用[J].四川省卫生管理干部学院学报,2005,24(2).
[106]CHEN Guifen,CAO Liying,WANG Guowei et.al.Application of spatial fuzzy clustering algorithm in precision fertilization[J].New Zealand Journal of Agricultural Research.2007,Vol 50:1249-1254.
[107]刘冬寒.改进的高维空间聚类算法及其在农业信息化中的应用[D].吉林大学,2005.
[108]张书慧.基于GPS、GIS的精确农业自动变量施肥理论与技术研究[D].吉林大学,2003,6:1-3.
[109]苗孝可,夏克俭,王秀.精准农业变量施肥智能决策支持系统的研究[J].计算机应用.2004,24(11):153-155.
[110]WANG Shengsheng,LIU Dayou,WANG Xinying et.al.Spatial Reasoning Based Spatial Data Mining for Precision Agriculture[C].HT Shen et al (Eds) APWeb Workshops 2006 LNCS 3842 SpringerVerlag Berlin Heidelberg.2006,506-510.
[111]张书慧,马成林,李伟,徐岩.变量施肥对玉米产量及土壤养分影响的试验[J].农业工程学报,2006,22(8):64-67.
[112]杨纶标,高英仪.模糊数学原理及应用[M].华南理工大学出版社,2004.
[113]陈桂芬,曹丽英,王国伟.加权空间模糊动态聚类算法在土壤肥力评价中的应用[J].中国农业科学,2009,42(10) :3559-3563.
[114]冯超.K-means聚类算法研究[D].大连理工大学,2007.
[115]代建华,李元香.粗集中属性约简的一种启发式遗传算法[J].西安交通大学学报,2002,36(12):286-290.
[116]何新贵.模糊数据库系统[M].北京:清华大学出版社,1994.
[117]李华华.粗糙集理论研究及其在隧道病害预测中的应用[D].北京交通大学,2007.
[118]Hu X.Knowledge Discovery in Databases:An Attribute-oriented Rough Set Approach[D].University of Regina,Canada,1995.
[119]洪雪飞.基于粗糙集的数据挖掘算法的研究与应用[D].北京交通大学,2008.
[120]马秀红,宋建社,董晟飞.数据挖掘中决策树的探讨[J].计算机工程与应用,2004,(01):185-214.
[121]王勇.时序数据挖掘技术及其在水质预测中的应用研究[D].广东工业大学,2005.
[122]S Chaudhuri,U Fayyad,J Bernhardt. Scalable Classification over SQL Databases[A][C].In Proc ICDE99,Sydney:Australia IEEE Computer Society.1999,472-478.
[123]Manish Mehta,Jorma Rissanen,Rakesh Agrawal.MDL based decision treepruning[C].International Conference on Knowledge Discovery in Databases and Data Mining,1995,216-221.
[124]马丽,陈桂芬.基于数据挖掘的决策树算法应用研究[J].农业网络信息,2008.11.
[125]ZhaoHui Tang,Jamie MacLennan.数据挖掘原理与应用SQL Server 2005数据库[M].北京:清华大学出版社,2007.
[126]王晓晔.时间序列数据挖掘中相似性和趋势预测的研究[D].天津大学,2003.
[127]王波,郭夜白,高来斌,仲崇文等.最优加权预测在吉林省粮食产量预测中的应用[J].吉林农业大学学报,2008,30(5):760-763.
[128]郑煜,孟军等.时间序列模型对黑龙江省水稻比较优势的预测[J].东北林业大学学报,2008,36(10):65-66.
[129]杨建萍.基于ARIMA模型的用电量时间序列建模和预报[J].工程数学学报,2008,25(04):611-615.
[130]梁鑫,庞丽,彭冬梅.桂林市汽车销售量的时间序列预测模型[J].广西科学,2008,15(4):386-388.
[131]徐薇,黄厚宽,秦勇等.基于时空数据挖掘的铁路客流预测方法[J].北京交通大学学报,2004,28(5):16-19.
[132]陈茹雯,黄仁,史金飞等.线性/非线性时间序列模型一般表达式及其工程应用[J].东南大学学报(自然科学版),2008,38(6):1077-1080.
[133]韩家炜,Kamber M著.数据挖掘概念与技术[M].北京:机械工业出版社,2001.
[134]段江娇.基于模型的时间序列数据挖掘—聚类和预测相关问题研究[D].复旦大学,2008.
[135]薛惠锋,张文宇,寇晓东.智能数据挖掘技术[M].西安:西北工业大学出版社,2005.
[136]王佐成,薛丽霞,李永树,徐京华.空间数据挖掘知识的地图可视化表达[J].计算机应用研究,2006,(2):253-255.
[137]Alfred InselBerg.MULTIDIMENSIONAL DETECTIVE[C].Proc.IEEE Symp.Information Visualization,1997,100-107.
[138]谢高地,陈沈斌.环境的空间连续变异与精准农业[M].北京:气象出版社,2005.
[139]邻伦,张晶,赵伟等.地理信息系统[M].北京:电子工业出版社,2002.
[140]Odlnad J.Spatial autocorrelation[M].California:Sage Publicationsinc.1988.
[141]张志强.基于地理信息系统的配电网络拓扑分析[D].中国电力科学研究院,2005.
[142]梁艳平,钟耳顺,朱建军.我国省级人均GDP增量变化的空间特征分析[J].中南大学学报(社会科学版),2003,19(3):355-359.
[143]胡彩平.基于自相关的空间数据挖掘若干关键技术的研究{D].南京航空航天学,2007.
[144]赵晋芳.山西省HIV/AIDS结核感染监测资料预测方法的研究[D].山西医科大学,2009.
[2]苗玉彬.精准农业智能测产系统的研究与应用[D].上海:上海交通大学,2003.
[3]冯红伟.数据挖掘技术的研究与应用[D].西安:西北工业大学,2002.6
[4]李德仁,关泽群空间信息系统的集成与实现[M].武汉:武汉大学出版社,2000.
[5]周海燕.空间数据挖掘的研究[D],北京:中国人民解放军信息工程大学,2003.4
[6]龙腾芳.数据挖掘技术在农业领域中的应用研究[J],微计算机信息,2005,35(13):40-53.
[7]栾家辉.车载GPS测产系统入校正装置[D],哈尔滨:东北农业大学,2003.1.
[8]郑良永,曹启民,夏炜林,胡剑非.精准农业发展趋势及其在我国的应用[J].华南热带农业大学学报,2005.01.
[9] Ng R T,Han J,Efficient and Effective Clustering Methods for Spatial Data Mining[C].In The 20th Very Large Databases Conference Santiago Chile.1994.
[10]Li D R,Cheng T. KDG Knowledge Discovery from GIS[C].In Proceedings of the Canadian Conference on GIS.Ottawa,1994.
[11]胡彩平,秦小麟.空间数据挖掘研究综述[J].计算机科学技术,2007,34(5):14-19.
[12] Shashi S,Chawla S.空间数据库[M].谢昆青,马修军,杨冬青等译.北京:机械工业出版社.2004
[13]李永森.SDMKD及智能空间决策支持系统研究[D],安徽:合肥工业大学,2006.7.
[14]樊明辉.空间数据挖掘及其可视化系统若干关键技术研究[D].中国科学院,2006.6.
[15]涂建东,陈崇成,樊明辉等.基于Java3D的空间关联规则可视化原理与实现[J].高技术通讯.2004(6):98-99.
[16] Ng R,Han J .Efficient and effective clustering method for spatial data mining Porc1994[J]. Int Conf Very Large Data bases(VLDB’94).1994:144-155.
[17] EsterMartinHnasPeter,KriegelJorg,SanderXiaowei Xu.A DensityBased Algorithm for Discovering Clusters in Lagre Spatial Daatbases with Noise Proceedings of the 2nd Intenrational Conferernce Knowledge Discovey and Data Mining(KDD96)Portland OregonAugust 1996.
[18]李德仁,王树良,李德毅等.空间数据挖掘理论与应用[M].北京:科学出版社,2006.
[19]韩家炜.数据挖掘:概念与技术[M].北京:机械工业出版社,2007.6.
[20]张雪伍,苏奋振,石忆邵等.空间关联规则挖掘研究进展[J].地理科学进展,2007,11:122-125.
[21]Robert Bembenik,Henryk Rybiski.Mining Spatial Association Rules with No Distance Parameter Intelligent Information Processing and Web Mining[C].Springer Berlin/Heidelberg.2006,35:499-508.
[22]M Ester, H P Kriegel,J Sander. Spatial data mining:A database approach[J].In Advances in Spatial Databases, 1997,1262:47-66.
[23]刘大有,王生生,虞强源等.基于定性空间推理的多层空间关联规则挖掘算法[J].计算机研究与发展.2004,41(4):565-570.
[24]LAN Rongqing,LIU Zengliang,YANG Xiaomei.Methods of Mining Fuzzy Spatial Association Rules[J].Journal of Institute of Surveying and Mapping.2005,22(1):36-39.
[25]Beaubouef Theresa,Petry Frederick E.A rough set foundation for spatial data mining involving vague regions[C].IEEE International Conference on Plasma Science. 2002,1:767-772.
[26]Beaubouef Theresa,Ladner Roy,Petry Frederick.Rough Set Spatial Data Modeling for Data Mining International[J].Journal of Intelligent Systems Granular Computing andData Mining.2004,19(7):567-584.
[27]Seo EunKyoung,Biggerstaff Michael I.Impact of cloud model microphysics on passive microwave retrievals of cloud properties Part II Uncertainty in rain hydrometeor structure and latent heating retrievals[J].Journal of Applied Meteorology and Climatology.2006,45(7):955-972.
[28]Yang Bin Zhu,ZhongYing.Mining multilevel spatial association rules with cloud models[J].Journal of Harbin Institute of Technology (New Series).2005,12(3):314-318.
[29]Wang Xiao Hui,Xie Jiancang,Li Jianxun et al.Implementation of GIS spatial data mining based on cloud theory[C].Proceedings 2006 International Conference on Hybrid Information Technology ICHIT 2006.2006,1:530-534.
[30]L K Sharma,O P Vyas,U S Tiwary et al.A Novel Approach of Multilevel Positive and Negative Association Rule Mining for Spatial Databases Machine Learning and Data Mining in Pattern Recognition[C].Springer Berlin/Heidelberg.2005,3(587):620-629.
[31]William Perrizo,Qin Ding,Qiang Ding et al.Deriving High Confidence Rules from Spatial Data Using Peano Count TreesIn Proc Advances in WebAge Information Management[C].Springer Berlin/Heidelberg.2001,2(118):91-102.
[32]吴习宇.粗糙集理论在农业决策支持系统知识发现中的应用[D].西南农业大学.2005.
[33]Aijun AnNing,ShanChristine,ChanNick et al.Discovering Rules for Water Demand Prediction[C]. An Enhanced Rough Set Approach Artificial Inetlligence.1996, 9(6):645-653.
[34]GrzymalaBusse.JWLERSA System of Knowledge Discovery Based on Rough[C]SetsIn Proc of 5th Intl Workship RSFD 96Tokyo.1996:443-444.
[35]梁本哲.基于粗糙集理论的土地利用规划决策支持系统模型及其应用研究[D].中国地质大学.2007.
[36]熊新惠.粗糙集理论在银行数据分析中的利用[D].安徽大学.2005.
[37]梁西燕.基于粗糙集理论的农业决策支持系统研究[D].西北农林科技大学.2008.
[38]郭红波,周明全,耿国华,王小凤.粗糙集理论分析及其在农业数据中的应用[J].计算机技术与发展.2007(10):165-167,188.
[39]朱绍文等.决策树采掘技术及发展趋势[J].计算机工程.2002,28(8):77-78.
[40]史忠植.知识发现[M].北京:清华大学出版社,2002,12.
[41]成文丽.基于决策树的数据挖掘算法的技术研究[D].太原理工大学.2003.
[42]Quinlan J R.C4.5 Programs for Machine Learning[M].Morgan Kaufmann.1992.
[43]R Groth.数据挖掘构筑企业竞争优势[M].侯迪等译.西安:西安交通大学出版社,2001:146-186.
[44]Fayyad U M et al.1996 Advances in Knowledge Discovery and Data Mining Menlopark CA AAAI/MIT Press Frasconi P Gori M Soda G 1999 Data categorization using decision trellises[C].IEEE Transactions on Knowledge and Data Engineering. 1996,11(5):697-712.
[45]Marsala C,Bigolin N M. Spatial data mining with fuzzy decision trees[J].In Data Mining edited by Ebecken N F F (Ashurst Lodge; UK WIT Press/Computational Mechanics Publications).1998:235-248.
[46]Cantu Paz, E Kmaath C.Indueing oblique decision trees with evolutionary Algorithms[J] IEEE Transaetions on Evolutionary Computation.2003,7(l):54~68.
[47]Stone P,Veloso M.Using decision tree confidence factors formultiagent control[C]. Minnesota,USA Proeeedings of the 2nd International Conference on Autonomous Agents.1998.
[48]Koperski K. A Progressive Refinement Approach to Spatial Data Mining[D] Ph D Thesis British Columbia Simon Fraser University. 1999.
[49]孙微微,胡月明,刘才兴等.基于决策树的土壤质量等级研究[J]华南农业大学学报(自然科学版) 2005.03.
[50]薛正平,邓华,杨星卫等.基于决策树和图层叠置的精准农业产量图分析方法[J].农业工程学报,Vol 22,No 8,2006,8.140-144.
[51]王占全.基于地理信息系统空间数据挖掘若干关键技术的研究[D].淅江大学,2005.
[52]芮小平.空间信息可视化关键技术研究——以25维、三维、多维可视化为例[D].中国科学院.2004.
[53]Ankerst M.et al.1999.3D shape histograms for similarity search and classification in spatial database[C].Lecture Notes in Computer Science 1651;207-225.
[54]Maceachren A M.et al. Constructing knowledge from multivariate spatiotemporal dataintegrating geographical visualization with knowledge discovery in database methods International[J]. Joural of Geographical Information Science 1999,13(4):311-334.
[55]Michael T. Rosenstein,Paul R. Cohen,Concepts from time series, Proc. Of the fifteenth national/tenth Conference on Artificial intelligence/Innovative apPlications of artificial intelligence,Madison,Wisconsin,Untied States,July 1998.739-745.
[56]F.Tkens. Detecting strange attractors in turbulence,Proc.of Dynamical Systems and Turbulence,Waruick,1980.366-381.
[57]Yoon J P,Lee J,Kim S. Trend Similarity and perdiction in time-series database[A]. Proc of SPIE on Data Mining and Knowledge Discovery:Theory Tools,and Technology II[C]. Washington:SPIE,2000.201-212.
[58]G. Das, K.Lin, H.Mannila, G.Renganathan, P.Smyth. Rule Discovery form time series, Proceedings of the Fourth International Conference on Knowledge Diseovery and Data Mining(KDD-98), AAAIPress, 1998.16-22.
[59]欧阳为民,蔡庆生.数据库中的时态数据挖掘研究[J].计算机科学.1998,25(4):60-63.
[60]刘念祖.时态数据挖掘的探讨[J].上海第二工业大学学报. 2001(2):27-31.
[61]张保稳.时间序列数据挖掘研究[D].西北工业大学.2002.
[62]Dempster A P, Laird N M ,Rubin D B. Maximum likelihood from incomplete data via the EM algorithm[J]. Journal of Royal Statistical Society ,1977 , B(39) :1-38.
[63]Ambroise C,Govaert G. Convergence of an EMtype algorithm for spatial clustering Pattern[C].Recognition Letters 1998,19(10):919-927.
[64]张瑞菊,陶华学.基于模糊神经网络的空间数据挖掘方法研究[J].山东科技大学学报(自然科学版),2003,22(4):24-27.
[65]Piatetsky-shapiro G.,1994. An overview of knowledge discovery in databases:recent progress and challenges In Rough Sets,Fuzzy Sets and Knowledge Discovery[C]. edited by Wojciech P. Niarko.Berlin:Spring-Verlag,1-10.
[66]王树良.基于数据场与模型的空间数据挖掘和知识发现[D].武汉大学,2002.
[67]李雄飞,李军等编著.数据挖掘与知识发现[M].北京:高等教育出版社,2003.
[68]汪杭军.基于粗糙集的空间数据挖掘研究[D].贵州大学,2006.
[69]张建军.GPS与GIS技术在ITS系统中的应用研究[D].长安大学,2004.
[70]Clark PNiblet TT.The CN2 induction algorithm[J].Machine Leaning.1989(3):261-283.
[71]杨春成.空间数据挖掘中聚类分析算法的研究[D].解放军信息工程大学,2004.
[72]符晓蓉.空间数据挖掘不确定性的研究[D].武汉理工大学,2007.
[73]WANG Shengsheng,LIU Dayou,CAO Bin,LIU Jie.A subspace clustering algorithm for high dimensional spatial data[J].Computer Applications.2005,11.2615-2617.
[74]Koperski K,Han J.Discovery of Spatial Association Rules in Geographic Information Databases[C].In Proceedings of the 4th International Symposium on Large Spatial Databases(SSD95) Maine,1995,647-667.
[75]Koperski K,Adhikary J, Han J. Spatial Data Mining : Progress and Challenges. In SIGMOD’96 Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD’96)[C], Mont real , Canada ,1996.
[76]Ng R,Han J.Efficient and effective clustering method for spatial data mining[C].Porc 1994 Int Conf Very Large Data bases(VLDB’94).1994,144-155.
[77]Wang Lizhen,Xie Kunqing,Chen Tao,et al. Efficient discovery of multilevel spatial association rules using partitions[J].Information and Software Technology.2005, 4(7):829-840.
[78]刘君强,潘云鹤.挖掘空间关联规则的前缀树算法设计与实现[J].中国图象图形学报.2003,8(4):476-480.
[79]Shashi S,Yan Huang.Discovering spatial colocation patterns:Asummary of results[A].In Proceedings of the 7th International Symposium on Spatial and Temporal Databases[C].Redondo Beach,CA,2001,23-62.
[80]Clementini E,Felice P.D.Koperski K. Mining Multiple level Spatial Association Rules for Objects with a Broad Boundary[J]. Data and Knowledge Engineering,2000,34:251-270.
[81]Kacar E,Cicekli N.K.Discovery Fuzzy Spatial Association Rules[C].Data Mining and Knowledge Discovery:Theory,Tools and Technology IV. In Dasarathy B V,ed.Proceedings of SPIE, Vol4730,2002,94-102.
[82]Zadeh LA.Fuzzy Sets[J].Information and Control.1965,8(3):338-353.
[83]张宏宇.粗糙集理论及其在数据挖掘中的应用研究[D].山西大学,2003.
[84]曹付元.多值信息系统与决策表知识约简[D].山西大学,2004.
[85]张文修,吴伟志,梁吉业,李德玉.粗糙集理论与方法[M].北京:科学出版社,2001.
[86]高小康.粗糙集理论研究及其在工程和医学诊断中的应用[D].同济大学,2007.
[87]Ziarko W.Variable Precision Rough Set Model[J].Journal of Computer and Sciences.1993,46(1):39-59.
[88]Kryszkiewicz M.Rough set approach to incomplete information systems[J].Information Sciences.1998,11(2):39-49.
[89]陈文伟编著.决策支持系统教程[M].北京:清华大学出版社.2006.7.
[90]成文丽.基于决策树的数据挖掘算法的技术研究[D].太原理工大学,2003.
[91]黄晓芳.数据挖掘中决策树算法及其应用[J].兵工自动化,2005,(02):35-36.
[92]王振龙.时间序列分析[M].北京:中国统计出版社,2000.2.
[93]InmonW.H. Buiding the Data Warahouse(Second Edition )[M].New York:John Wiley & Sons,1996.
[94]Srivastsva J.,Cheng P.Y.warahouse creation-a potential roadblock to data warahousing[J].IEEE Transactions on Knowledge and Data Engineering.1999,11(1):118-126.
[95]史文中,王树良.GIS中属性不确定性的处理方法及其发展[J].遥感学报,2002.5.
[96]李琦,杨超伟.空间数据仓库及其构建策略[J].中国图象图形学报,1999,4[A](11):984-990.
[97]杨群,闾国年,陈钟明.地理信息数据仓库的技术研究[J].中国图象图形学报,1999,4[A](8):621-626.
[98]Zhou X.,Truffet D.,Han J.Efficient polygon amalgamation methods for spatial OLAP and spatial data mining[C].Lecture Notes in Computer Science,1999,16(51):167-187.
[99]Yao Y. Y. ,Wong S. K. M. , Lin T. Y. A Review of Rough Set Models[C].In Rough Sets and Data Minning Anakysis for Imprecise Data, edited by Y. LIN and N.CERCONE. London:Kluwer.Academic Publishers,1997,47-75.
[100]扈立家,唐雪漫.辽宁省构建精准农业技术体系问题研究[J].辽宁行政学院学报,2006年3期.
[101]Jiawei Han,Micheline Kamber.Data Mining Concept and Techniques[M].China Machine Press.2005,223-232.
[102]ZHU Qiang,WANG Ye.Fuzzy Clustering Analysis and Its Application in Data Mining[J].Modern Computer.2007,4:87-88.
[103]Shengsheng WANG,Dayou LIU, Xinying WANG, Jie LIU. Spatial Reasoning Based Spatial Data Mining for Precision Agriculture[C]. H.T. Shen et al. (Eds.):APWeb Workshops 2006, LNCS 3842.SpringerVerlag Berlin Heidelberg.2006,506–510.
[104]Xu R..Survey of clustering algorithms[C].IEEE Trans. Neu. Netw.2005,16(3):645-678.
[105]罗兰星.模糊聚类分析中传递闭包法及其应用[J].四川省卫生管理干部学院学报,2005,24(2).
[106]CHEN Guifen,CAO Liying,WANG Guowei et.al.Application of spatial fuzzy clustering algorithm in precision fertilization[J].New Zealand Journal of Agricultural Research.2007,Vol 50:1249-1254.
[107]刘冬寒.改进的高维空间聚类算法及其在农业信息化中的应用[D].吉林大学,2005.
[108]张书慧.基于GPS、GIS的精确农业自动变量施肥理论与技术研究[D].吉林大学,2003,6:1-3.
[109]苗孝可,夏克俭,王秀.精准农业变量施肥智能决策支持系统的研究[J].计算机应用.2004,24(11):153-155.
[110]WANG Shengsheng,LIU Dayou,WANG Xinying et.al.Spatial Reasoning Based Spatial Data Mining for Precision Agriculture[C].HT Shen et al (Eds) APWeb Workshops 2006 LNCS 3842 SpringerVerlag Berlin Heidelberg.2006,506-510.
[111]张书慧,马成林,李伟,徐岩.变量施肥对玉米产量及土壤养分影响的试验[J].农业工程学报,2006,22(8):64-67.
[112]杨纶标,高英仪.模糊数学原理及应用[M].华南理工大学出版社,2004.
[113]陈桂芬,曹丽英,王国伟.加权空间模糊动态聚类算法在土壤肥力评价中的应用[J].中国农业科学,2009,42(10) :3559-3563.
[114]冯超.K-means聚类算法研究[D].大连理工大学,2007.
[115]代建华,李元香.粗集中属性约简的一种启发式遗传算法[J].西安交通大学学报,2002,36(12):286-290.
[116]何新贵.模糊数据库系统[M].北京:清华大学出版社,1994.
[117]李华华.粗糙集理论研究及其在隧道病害预测中的应用[D].北京交通大学,2007.
[118]Hu X.Knowledge Discovery in Databases:An Attribute-oriented Rough Set Approach[D].University of Regina,Canada,1995.
[119]洪雪飞.基于粗糙集的数据挖掘算法的研究与应用[D].北京交通大学,2008.
[120]马秀红,宋建社,董晟飞.数据挖掘中决策树的探讨[J].计算机工程与应用,2004,(01):185-214.
[121]王勇.时序数据挖掘技术及其在水质预测中的应用研究[D].广东工业大学,2005.
[122]S Chaudhuri,U Fayyad,J Bernhardt. Scalable Classification over SQL Databases[A][C].In Proc ICDE99,Sydney:Australia IEEE Computer Society.1999,472-478.
[123]Manish Mehta,Jorma Rissanen,Rakesh Agrawal.MDL based decision treepruning[C].International Conference on Knowledge Discovery in Databases and Data Mining,1995,216-221.
[124]马丽,陈桂芬.基于数据挖掘的决策树算法应用研究[J].农业网络信息,2008.11.
[125]ZhaoHui Tang,Jamie MacLennan.数据挖掘原理与应用SQL Server 2005数据库[M].北京:清华大学出版社,2007.
[126]王晓晔.时间序列数据挖掘中相似性和趋势预测的研究[D].天津大学,2003.
[127]王波,郭夜白,高来斌,仲崇文等.最优加权预测在吉林省粮食产量预测中的应用[J].吉林农业大学学报,2008,30(5):760-763.
[128]郑煜,孟军等.时间序列模型对黑龙江省水稻比较优势的预测[J].东北林业大学学报,2008,36(10):65-66.
[129]杨建萍.基于ARIMA模型的用电量时间序列建模和预报[J].工程数学学报,2008,25(04):611-615.
[130]梁鑫,庞丽,彭冬梅.桂林市汽车销售量的时间序列预测模型[J].广西科学,2008,15(4):386-388.
[131]徐薇,黄厚宽,秦勇等.基于时空数据挖掘的铁路客流预测方法[J].北京交通大学学报,2004,28(5):16-19.
[132]陈茹雯,黄仁,史金飞等.线性/非线性时间序列模型一般表达式及其工程应用[J].东南大学学报(自然科学版),2008,38(6):1077-1080.
[133]韩家炜,Kamber M著.数据挖掘概念与技术[M].北京:机械工业出版社,2001.
[134]段江娇.基于模型的时间序列数据挖掘—聚类和预测相关问题研究[D].复旦大学,2008.
[135]薛惠锋,张文宇,寇晓东.智能数据挖掘技术[M].西安:西北工业大学出版社,2005.
[136]王佐成,薛丽霞,李永树,徐京华.空间数据挖掘知识的地图可视化表达[J].计算机应用研究,2006,(2):253-255.
[137]Alfred InselBerg.MULTIDIMENSIONAL DETECTIVE[C].Proc.IEEE Symp.Information Visualization,1997,100-107.
[138]谢高地,陈沈斌.环境的空间连续变异与精准农业[M].北京:气象出版社,2005.
[139]邻伦,张晶,赵伟等.地理信息系统[M].北京:电子工业出版社,2002.
[140]Odlnad J.Spatial autocorrelation[M].California:Sage Publicationsinc.1988.
[141]张志强.基于地理信息系统的配电网络拓扑分析[D].中国电力科学研究院,2005.
[142]梁艳平,钟耳顺,朱建军.我国省级人均GDP增量变化的空间特征分析[J].中南大学学报(社会科学版),2003,19(3):355-359.
[143]胡彩平.基于自相关的空间数据挖掘若干关键技术的研究{D].南京航空航天学,2007.
[144]赵晋芳.山西省HIV/AIDS结核感染监测资料预测方法的研究[D].山西医科大学,2009.