基于可拓数据挖掘的用户需求获取研究  

Research on customer requirement capturing based on extension data mining

作　　者：刘斌[1,2] 朱明[3] 王景华[1,4] 张利[3] 李献会[4] 

机构地区：[1]合肥工业大学计算机与信息学院,安徽合肥230009 [2]齐鲁师范学院计算机系,山东济南250002 [3]合肥工业大学机械与汽车工程学院,安徽合肥230009 [4]洛阳轴研科技股份有限公司,河南洛阳471039

出　　处：《合肥工业大学学报（自然科学版）》2011年第12期1823-1826,共4页Journal of Hefei University of Technology：Natural Science

基　　金：国家科技支撑计划资助项目(201113AF091301);高等学校博士学科点专项科研基金资助项目(20080359028)

摘　　要：为了准确、有效地获取潜在的用户需求信息,文章提出了一种基于可拓数据挖掘的关联规则挖掘方法,通过将用户需求按不同需求分类,构成具有不同功能属性的用户需求集;利用粒子群算法挖掘出用户需求集中的关联规则,结合可拓变换原理,推理出新的关联规则知识,通过实例验证了方法的有效性和可行性。In order to accurately and effectively access the potential customer requirement information, an association rule mining method based on extension data mining is proposed. Customer requirements are classified according to different requirement categories, and the multiple customer requirement sets with different functions are established. The association rules of customer requirement sets are mined by using the particle swarm algorithm, and then the new association rules knowledge is deduced based on the extension transformation principle. The validity and feasibility of the method are verified by examples.

关 键 词：用户需求 数据挖掘 关联规则 可拓变换 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]