【作者】 陈亮；

【导师】 谢戈；

【作者基本信息】 云南大学 ， 计算机技术， 2013， 硕士

【摘要】 21世纪是个信息爆炸的世纪,借助互联网的发展,当代的信息量不断增加,已经成几何级别的增长。对这些与日俱增的海量数据处理和分析技术的研发和优化是个长期艰巨的挑战,同时海量数据中隐含的宝贵知识模式又会推动企业现代化的发展。数据挖掘技术也应运而生,该技术在海量数据中发现有用知识模式,最终将协助决策者依据分析处理的可靠模式来处理企业各阶段面临的决策问题,从而提高现代企业的竞争能力。本文的分析是基于据挖掘现状的基础上,在解决对数据预测相关问题时,通常采用分类技术。但是传统的数据挖掘技术并不能有效地反映数据之间普遍具有因果联系这一特性,针对这一特点,本文采用协方差与相关系数特性构造多维数据间逻辑关系,并且与信息熵理论基础上的决策树算法相结合,优化传统决策树算法提高了数据挖掘的准确性和实用性,并经过实验对比分析得出实验结果,分析出该改进算法的性能、改进算法所适用的环境及改进算法的特点。本文最后将改进后的算法应用于现流行的电子商务系统中分支网上家居系统中,并对客户购买家居产品模式进行分析,构造出改进算法生成的决策树,并对最后数据挖掘的结果进行模式评估,分析出客户的购买模式特征和顾客的购买兴趣,用来验证改进算法的正确性。更多还原

【Abstract】 The21st century is the century of information explosion With the rapid development of World Wide Web, the amount of information is increasing fast, and it has become the geometric level of growth. Development and optimization of increasing mass data processing and analysis technology is a long-term and arduous challenge. Meanwhile, massive amounts of data implied valuable knowledge model will promote the development of modern enterprises. Data mining techniques also came into being, the technology in the massive amounts of data to find useful knowledge that will eventually assist decision makers to deal with the decision-making problems faced by the various stages of the enterprise based on reliable analytical processing mode. Thereby to help the modern enterprises enhance their competitiveness.In this paper, the analysis is based on the basic of the present situation of data mining in the resolving related problems of data forecast. We usually use the technology of classification. But traditional data mining techniques cannot effectively reflect the feature of the data that generally have relations in each other. According to this feature,in this paper taking the measure of Co variance and correlation coefficient to construct the logical relationship in multi-dimensional data, and combining with decision tree algorithm that based on information entropy theory. This algorithm optimizes the traditional decision tree algorithm and improve the accuracy and usefulness of data mining. With experimental comparison analysis of the experimental results, and analysis of the improved performance of the algorithm, it comes to conclusions features of the performance of this algorithm and what environment is fit this algorithm. In the end,put the improved algorithm into the online Home Decor system which is a branch o f popular e-commerce system. Analyzing the schema ofbuying Home Decor, and constructing the improved decision tree.Assessing the result of data mining with the improved decision tree, and coming to a conclusion of the customer buying habit and buying interest.In the end using the data verify the correctness of using this improved algorithm.更多还原