C2C网商决策支持系统研究—基于数据分解融合与案例学习集成

随着电子商务的快速发展，面向复杂数据挖掘的C2C网商决策支持系统的研发具有急迫性。本项目旨在研究C2C网商决策支持系统的构建与开发问题，从复杂系统的构建角度出发，运用演绎法和归纳法结合的思路。首先运用经验模态分解方法研究浏览量受重大事件、节假日、天气等因素影响的时变特征，并进行离散化构建决策规则；然后研究与C2C网商相关的不同类型数据的软集合表示，并用案例库中的数据构建训练样本集，提出基于C2C网商案例学习的软集合决策规则获取模型；最后结合时间、场景、偶发事件等因素，研究C2C网商决策支持系统的系统构架，并开发C2C网商决策支持系统原型。本项目的理论研究成果可以为C2C网商决策支持系统设计提供理论思路，并丰富面向异值非结构化数据的软集合数据挖掘方法。同时，系统原形可进一步开发推广为C2C网商提供服务，并促进电子商务咨询服务业的发展。还可为C2C网商的人力资源、管理理论、企业构架提供新思路。

With the rapid development of e-commerce, complex data mining system for the development of C2C online decision support is very important. The project aims at how to construct and develop C2C online decision support system. It is based on the angle of how to construction of complex systems with the use of the combination of the ideas of deductive and inductive method. First, we study the time-varying characteristics of visitors number that is affected by of the major events, holidays, weather factors by empirical mode decomposition method, and construct the visiting volumn and visitor number predict model by using the result of empirical study; secondly we study the representation of soft set for multi-types data of C2C network operators related, and propose the decision rules obtain method based on soft set decision making system. Finally we combine time, scenes, episodic events and other factors, and study the architecture of C2C network operators decision-support system, and develop prototype for it. The results of theoretical studies of the project for the C2C online decision support system can provide theoretical ideas and enrich soft set theory and large data environment database designing theory and data mining methods. At the same time, the system prototype can be further developed to provide services for C2C network operators. It can also provide ideas for the C2C network operators on human resources, management theory, enterprise architecture etc.