基于可拓集的可拓分类知识获取研究  

Acquiring Extension Classification Knowledge Based on Extensible Set

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》2008年第16期184-191,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);广东省自然科学基金项目(05001832)

摘　　要：以可拓集理论为依据,给出基于可拓变换的可拓分类知识的定义,并在信息元集和评价信息元集的基础上,探讨可拓分类知识的获取方法,包括质变域知识的获取、量变域知识的获取和有关拓界的知识的获取.这是可拓数据挖掘的主要内容之一,为从数据库中获取变化的分类知识提供了新的思路.According to extensible set theory,the definition of extension classification based on extension transformation is presented. The acquiring methods on extension classification knowledge are discussed on information-element set and evaluating information-element set basis, including the knowledge of qualitative change field, quantitative change field and extension boundary. This is one of central contents of extension data mining. The study provides a new idea for acquiring changing classification knowledge from among database.

关 键 词：可拓集 关联函数 可拓分类 可拓数据挖掘 

分 类 号：O189.1[理学—数学]