基于支持向量机的空间数据挖掘方法及其在旅游地理经济分析中的应用
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摘要
本论文根据旅游地理经济分析预测管理需求，就基于支持向量机的空间数据挖掘分类或回归的理论与方法以及在旅游地理经济管理中的应用展开了系统研究，建立了基于支持向量机的空间数据分类或回归挖掘模型与算法、特征选择算法，设计实现了面向旅游地理经济应用的数据挖掘软件。主要内容如下：
（1）提出了SVM若干算法。通过组合优化方法和最小二乘方法，以及多分类支持向量机方法，提出了MC-COLS-SVM分类机算法；通过组合优化方法，以及减少约束，降低问题复杂度，提出了组合优化COLS-BSVR回归机算法。提出了对于支持向量回归机特征选择的算法，并进行了实证分析。
（2）构建了基于支持向量机的空间数据挖掘理论与方法体系。研究设计了基于支持向量机的空间数据挖掘工作流程与框架以及实现方法；基于MC-COLS-SVM多分类组合优化思路，设计了空间数据分类算法；基于COLS-BSVR组合优化最小二乘支持向量回归机思路，设计了空间数据回归算法。
（3）提出了时政指数、景区景点分布指数，并成功应用于旅游地理经济分析之中。通过对旅游收入、游客人数、时政指数、景区景点分布指数、GDP、CPI等变量时间序列的统计描述分析及其它们对旅游地理经济影响分析，提取了旅游地理经济数据特征，设计了相应的旅游地理经济数据库。
（4）建立了基于支持向量机的旅游地理经济预测模型。基于提出的COLS-BSVR支持向量回归机算法，建立了基于支持向量机的旅游地理经济分析预测数学模型；设计了数据挖掘中的数据构造模式，验证了模型与模式的有效性。
（5）建立了基于支持向量机的旅游地理经济风险管理模型。基于设计的空间数据分类算法、回归算法以及特征选择算法，结合旅游地理经济特征敏感性分析，建立了风险管理数学模型，验证了模型的有效性。
（6）设计并实现了基于支持向量机的旅游地理经济数据挖掘软件。该数据挖掘软件分三层结构构建；各种数据采集预处理后，存入旅游地理经济数据库，通过基于支持向量回归机的算法运算，生成预测信息，供分析决策参考。
该论文有图51幅，表44个，参考文献116篇。
This Dissertation is based on the economic characteristic of tourism geography,the needs for economic analysis management of tourism geography. Using the theoryand method of classification or regression algorithms for spatial data mining which isthe application of support vector machine, systematic researching on the spatial datamining method of support vector machine and its application on tourism geographyeconomy, some innovation achievement have been accomplished, listed below:
(1) Define severral SVM algorithms. Using combinatorial optimization and leastsquare method, and Multi-class Support Vector Machine method, theMC-COLS-SVM classification algorithm has been raised; using combinatorialoptimization method, reducing limitation and complexity of problems, thecombinatorial optimization COLS-BSVR regression algorithm has been raised.Coming up with the feature selection algorithm of support vector regression, and alsoconducted the confirmed analysis.
(2) Applying the SVM classification and regression method and theory ontospatial data mining, the spatial data mining theory and method system has beenconstructed. The working procedure and framework of spatial data mining have beendesigned and built, based on the supporting vector machine. Using MC-COLS-SVM(Multi-class Combinatorial Optimization Least Squares Support Vector Machine)optimization idea, the spatial data classification algorithm has been designed.Referring to the idea of COLS-BSVR (Combinatorial Optimization Least SquaresSupport Vector Regression), the spatial data regression algorithm has been designed.
(3) Define the index of events and policies and the index of scenic areadistribution. They are widely used in the tourism geography economy. By means oftime series analysis and statistical analysis, focusing on the index of scenic areadistribution, the influence of GDP, CPI on tourism geography economy, and theimpact of events, policies, we have analyzed and extracted the characters of tourismgeography economy. The corresponding tourism geography economy database hasbeen built.
(4) The design of the forecasting model of tourism geography economy. Bymeans of COLS-BSVR support vector regression algorithm, creatively built theanalytical forecasting math model which is based on supporting vector machine forthe tourism geography economy. The data structure mode in data mining has been designed; also the effectiveness of analytical forecasting math model has been proved.
(5) The design of the risk management model of tourism geography economy.Referring to the MC-COLS-SVM algorithm, COLS-BSVR algorithm and featureselection algorithm, analyzing and extracting the risk characteristics of tourismgeography economy, creatively built the risk management math model, with itseffectiveness being verified.
(6) The design and realization of the forecasting platform of tourism geographyeconomy. This platform has three parts. After collecting and preprocessing mass data,save them into tourism geography economy database, then generate forecastinginformation with the help of supporting vector regression algorithm. This informationwill be used for analysis and decision making.
This paper contains51Figures,44tables,116references.
（1）提出了SVM若干算法。通过组合优化方法和最小二乘方法，以及多分类支持向量机方法，提出了MC-COLS-SVM分类机算法；通过组合优化方法，以及减少约束，降低问题复杂度，提出了组合优化COLS-BSVR回归机算法。提出了对于支持向量回归机特征选择的算法，并进行了实证分析。
（2）构建了基于支持向量机的空间数据挖掘理论与方法体系。研究设计了基于支持向量机的空间数据挖掘工作流程与框架以及实现方法；基于MC-COLS-SVM多分类组合优化思路，设计了空间数据分类算法；基于COLS-BSVR组合优化最小二乘支持向量回归机思路，设计了空间数据回归算法。
（3）提出了时政指数、景区景点分布指数，并成功应用于旅游地理经济分析之中。通过对旅游收入、游客人数、时政指数、景区景点分布指数、GDP、CPI等变量时间序列的统计描述分析及其它们对旅游地理经济影响分析，提取了旅游地理经济数据特征，设计了相应的旅游地理经济数据库。
（4）建立了基于支持向量机的旅游地理经济预测模型。基于提出的COLS-BSVR支持向量回归机算法，建立了基于支持向量机的旅游地理经济分析预测数学模型；设计了数据挖掘中的数据构造模式，验证了模型与模式的有效性。
（5）建立了基于支持向量机的旅游地理经济风险管理模型。基于设计的空间数据分类算法、回归算法以及特征选择算法，结合旅游地理经济特征敏感性分析，建立了风险管理数学模型，验证了模型的有效性。
（6）设计并实现了基于支持向量机的旅游地理经济数据挖掘软件。该数据挖掘软件分三层结构构建；各种数据采集预处理后，存入旅游地理经济数据库，通过基于支持向量回归机的算法运算，生成预测信息，供分析决策参考。
该论文有图51幅，表44个，参考文献116篇。
This Dissertation is based on the economic characteristic of tourism geography,the needs for economic analysis management of tourism geography. Using the theoryand method of classification or regression algorithms for spatial data mining which isthe application of support vector machine, systematic researching on the spatial datamining method of support vector machine and its application on tourism geographyeconomy, some innovation achievement have been accomplished, listed below:
(1) Define severral SVM algorithms. Using combinatorial optimization and leastsquare method, and Multi-class Support Vector Machine method, theMC-COLS-SVM classification algorithm has been raised; using combinatorialoptimization method, reducing limitation and complexity of problems, thecombinatorial optimization COLS-BSVR regression algorithm has been raised.Coming up with the feature selection algorithm of support vector regression, and alsoconducted the confirmed analysis.
(2) Applying the SVM classification and regression method and theory ontospatial data mining, the spatial data mining theory and method system has beenconstructed. The working procedure and framework of spatial data mining have beendesigned and built, based on the supporting vector machine. Using MC-COLS-SVM(Multi-class Combinatorial Optimization Least Squares Support Vector Machine)optimization idea, the spatial data classification algorithm has been designed.Referring to the idea of COLS-BSVR (Combinatorial Optimization Least SquaresSupport Vector Regression), the spatial data regression algorithm has been designed.
(3) Define the index of events and policies and the index of scenic areadistribution. They are widely used in the tourism geography economy. By means oftime series analysis and statistical analysis, focusing on the index of scenic areadistribution, the influence of GDP, CPI on tourism geography economy, and theimpact of events, policies, we have analyzed and extracted the characters of tourismgeography economy. The corresponding tourism geography economy database hasbeen built.
(4) The design of the forecasting model of tourism geography economy. Bymeans of COLS-BSVR support vector regression algorithm, creatively built theanalytical forecasting math model which is based on supporting vector machine forthe tourism geography economy. The data structure mode in data mining has been designed; also the effectiveness of analytical forecasting math model has been proved.
(5) The design of the risk management model of tourism geography economy.Referring to the MC-COLS-SVM algorithm, COLS-BSVR algorithm and featureselection algorithm, analyzing and extracting the risk characteristics of tourismgeography economy, creatively built the risk management math model, with itseffectiveness being verified.
(6) The design and realization of the forecasting platform of tourism geographyeconomy. This platform has three parts. After collecting and preprocessing mass data,save them into tourism geography economy database, then generate forecastinginformation with the help of supporting vector regression algorithm. This informationwill be used for analysis and decision making.
This paper contains51Figures,44tables,116references.
引文
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