Development and Evaluation of Mathematical Models for Ubiquitous Data Mining

本研究的目的是提出泛在数据挖掘的概念，并建立其数学模型。普适数据挖掘是一种新的知识发现技术，适用于包括Web 2.0在内的先进普适IT环境。为了实现早期模型的最佳集成，我们开发了一种基于单类支持向量机（OC-SVM）的新算法，并在SAMO 2007国际会议上发表了这项研究的结果。首席研究员（M。Koda）受邀编辑日本运筹学会会刊（日文版）关于OC-SVM的特刊，并在同一期（第51卷第11期，第11页）上编辑和发表了我们的结果。677-682，2006）。我们进一步研究了提升技术作为合并多个数据源的手段，所述多个数据源包括来自诸如IC标签和RFID（射频识别）的无处不在的感测设备的数据。结果，我们得到了一个新的鲁棒集成学习算法，结果发表在日本运筹学学会杂志（第51卷，第2001页）。95-110，2008）。关于金融工程的随机灵敏度分析的研究成果也在ICCS 2007国际会议上进行了报告，并发表在Lecture Notes in Computer Science（No.4488，447-454，2007）上。基于支持向量机技术，我们探索了一种客户细分方法，并开发了一个普适的客户关系管理数据挖掘工具。我们针对传统采矿技术对该工具进行了原型设计和基准测试，结果发表在《欧洲运筹学杂志》上（第186卷，第190页）。358-379，2008）。我们与研究员和研究生一起在监督下完成了普适数据挖掘原型模型的初步设计。

The objective of this research was to propose a concept of ubiquitous data mining and develop its mathematical models. Ubiquitous data mining is a new knowledge discovery technology suited for advanced ubiquitous IT environment including Web 2.0. In order to achieve the optimal integration of earlier models, we developed a new algorithm based on the One-Class Support Vector Machine (OC-SVM), and the result of this investigation was presented at the international conference, SAMO2007. The head investigator (M. Koda) was invited to edit the special issue of the Transactions of the Operations Research Society of Japan (in Japanese) on OC-SVM, and complied and published our results in the same issue (vol. 51, No. 11, pp. 677-682, 2006).We further investigated boosting techniques as a means to incorporate multiple data sources including data from ubiquitous sensing devices such as IC tags and RFID (Radio Frequency Identification). As a result, we derived a new robust ensemble learning algorithm, and results were published in the Journal of the Operations Research Society of Japan (vol. 51, pp. 95-110, 2008). The research result on the stochastic sensitivity analysis of financial engineering was also reported at the international conference, ICCS2007, and published in the Lecture Notes in Computer Science (No. 4488, 447-454, 2007). Based on the support vector machine techniques, we explored a customer segmentation methodology and developed a ubiquitous data mining tool for customer relationship management. We prototyped and benchmarked the tool against conventional mining technologies, and the result was published in the European Journal of Operational Research (vol. 186, pp. 358-379, 2008).Jointly with the investigator and graduate students under supervision, we completed a preliminary design of the prototype model for ubiquitous data mining.