Synthesis and Optimization of Data Mining Models to Achieve Higher Performance

数据挖掘模型的综合和优化以实现更高的性能

• 批准号: 13680504
• 负责人: KODA Masato
• 金额: $ 2.18万
• 依托单位: University of Tsukuba
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2001
• 资助国家: 日本
• 起止时间: 2001 至 2002
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-13680504/
• 关键词: Data Mining; Stochastic Sensitivity; Local Search; Scoring Model; TSP; ダイレクトマーケティング; 局所最適化; CRM; 関数最適化; 勾配法; 局所探索法

We have studied the synthesis and optimization of mathematical models for data mining to achieve higher performance, and obtained the following results :1.A new stochastic optimization algorithm for discrete optimization :Based on stochastic sensitivity analysis techniques, a novel optimization algorithm using Gaussian white noise is developed for a class of discrete optimization problems. Unlike the familiar local search algorithms, the proposed method does not require time-consuming metaheuristics. The algorithm generates sample points with Gaussian white noise and, and yields gradient information that are needed in local search. By intensive numerical studies, it is shown that the method is effective in finding solutions for noisy objective landscapes with inexact gradient information including traveling salesman problem (TSP).2. Synthesis of data mining models to achieve high performance :A technical framework is developed to assess the performance of data mining models and synthesize them in order to construct a mixture of experts. Based on the boosting techniques, the proposed framework enables synthesis of the near-optimal mixture. The numerical study and analysis are conducted on real business data and it is validated that the framework is effective in practice.3.Applications to production scheduling and knowledge management :Applications of data mining are studied to develop production scheduling and knowledge management. The aspect of knowledge discovery is especially explored to build soft systems methodology in order to incorporate knowledge-creation theory.

我们研究了数据挖掘数学模型的综合和优化，以达到更高的性能，并获得了以下结果：1。一种新的离散优化随机优化算法：基于随机灵敏度分析技术，针对一类离散优化问题，提出了一种新的高斯白噪声优化算法。与常见的局部搜索算法不同，该方法不需要耗时的元启发式算法。该算法利用高斯白噪声和，生成样本点，并得到局部搜索所需的梯度信息。通过大量的数值研究表明，该方法可以有效地求解梯度信息不精确的有噪声客观景观问题，包括旅行推销员问题（TSP）。综合数据挖掘模型以实现高性能：开发了一个技术框架来评估数据挖掘模型的性能并综合它们以构建专家混合。基于增强技术，提出的框架能够合成接近最优的混合物。对实际业务数据进行了数值研究和分析，并在实践中验证了该框架的有效性。在生产调度和知识管理中的应用：研究了数据挖掘在生产调度和知识管理中的应用。在构建软系统方法论的过程中，着重探讨了知识发现方面的问题，以期与知识创造理论相结合。

项目成果

Hiroyuki Okano, Masato Koda: "An Optimization Algorithm Based On Stochastic Sensitivity Analysis For Noisy Objective Landscapes"京都大学 数理解析研究所講究録「確率数値解析における諸問題,V」. 1240. 47-57 (2001)

Hiroyuki Okano、Masato Koda：“An Optimization Algorithm Based on Stochastic Sensitivity Analysis For Noisy Objective Landscapes”京都大学数学科学研究所讲座记录“Problems in Stochastic Numerical Analysis，V”。 1240. 47-57 (2001)。

T.Yoshida: "Soft Systems Methodology Based on Organizational Knowledge Creation Theory"Systems Theory and Practice in the Knowledge Age. 1-6 (2002)

T.Yoshida：《基于组织知识创造理论的软系统方法论》知识时代的系统理论与实践。

Hiroyuki Okano, Masato Koda: "An Optimization Algorithm Based On Stochastic Sensitivity Analysis"Proceedings of Third International Symposium on Sensitivity Analysis of Model Output, SAMO 2001, European Commision. 177-181 (2001)

Hiroyuki Okano、Masato Koda：“基于随机敏感性分析的优化算法”第三届模型输出敏感性分析国际研讨会论文集，SAMO 2001，欧盟委员会。

H. Okano and M. Koda,: "An Optimization Algorithm Based on Stochastic Sensitivity Analysis for Noisy Objective Landscapes"Reliability Engineering and System Safety. 79, No. 2. 245-252 (2003)

H. Okano 和 M. Koda，：“基于噪声目标景观随机敏感性分析的优化算法”可靠性工程和系统安全。

M. Goto, K. Murayama, K. Monma, and M. Koda: "Development of A Scoring Model Using Data Mining Technology(in Japanese)"Journal of the Academic Society of Direct Marketing. 1, No. 1. 19-32 (2002)

M. Goto、K. Murayama、K. Monma 和 M. Koda：“利用数据挖掘技术开发评分模型（日语）”直复营销学会期刊。