可拓数据挖掘研究进展  

Recent Progress in Extension Data Mining

作　　者：杨春燕[1] 蔡文[1] 

机构地区：[1]广东工业大学可拓工程研究所,广东广州510090

出　　处：《数学的实践与认识》2009年第4期134-141,共8页Mathematics in Practice and Theory

基　　金：国家自然科学基金项目(70671031);广东省普通高校人文社会科学研究重点项目(06ZD63008)

摘　　要：可拓学研究用形式化模型解决矛盾问题的理论与方法,可拓数据挖掘是可拓学和数据挖掘结合的产物,它探讨利用可拓学方法和数据挖掘技术,去挖掘数据库中与可拓变换有关的知识,包括可拓分类知识、传导知识等可拓知识.随着经济全球化的推进,环境的多变促使了信息和知识的更新周期缩短,创新和解决矛盾问题越来越成为各行各业的重要工作.因此,如何挖掘可拓知识就成为数据挖掘研究的重要任务.研究表明,可拓数据挖掘将具有广阔的应用前景.将介绍可拓数据挖掘的集合论基础、基本知识和目前研究的主要内容,并提出今后需要进一步探讨的问题及其发展前景.Extenics is a new discipline for dealing with contradiction problems with formulize model. Extension data mining （EDM） is a product combining Extenics with data mining. It explores to acquire the knowledge about extension transformations in databases, which is called extension knowledge （EK）, taking advantage of extension methods and data mining technology. EK includes extension classification knowledge, conductive knowledge and so on. With the advance of economic globally, the renewal periods of information and knowledge are shortened because of environmental levity. Innovation and solving contradiction problems gradually turn to an important job for every walk of life. Therefore ,＂how to mine extension knowledge＂ becomes the important task of data mining research. The research indicated that EDM will have wide applied prospects. In this paper, the base of set theory,basic knowledge, and the main contents of EDM will be introduced. The problems that need to explore further and its developmental Prospects will be presented.

关 键 词：可拓数据挖掘 可拓集 可拓知识 可拓分类知识 传导知识 

分 类 号：O22[理学—运筹学与控制论]