METHODS OF ACQUIRING EXTENSIBLE CLASSIFICATION KNOWLEDGE

摘要：

Classification is the precondition for dealing with problems. The simplest classification is to divide the considered objects into two sorts: “yes and “no. Classical set is just the formalize description on this classification. Fuzzy set can describe the classification of fuzzy phenomena. The two classifications are all static. In realistic world, some classifying problems cannot be described by classical set or fuzzy set because the sort of an object is changeable. They lie on adopted transformations. The classification problems are abounding, so we must study the classifying methods based on transformations. Extensible set is a concept based on this sort of practical problems. Extensible classification studies the classified change and changing classification based on extensible set. Extensible classification knowledge is a sort of knowledge based on extension transformation. According to extensible set theory, the general steps on acquiring extensible classification knowledge are discussed, and its general flow is presented. The study provides an operable method for acquiring classification knowledge based on extension transformation in database.

关键词： extensible set dependent function extensible classification information-element extension data mining

作者: CHUN-YAN YANG WEN CAI

作者单位: Research Institute of Extension Engineering,Guangdong University of Technology,Guangzhou,China 510090

会议类型: 国际会议

会议名称: 2008高等智能国际会议(2008 International Conference on Advanced Intelligence)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-18（万方平台首次上网日期，不代表论文的发表时间）