Rules Extraction from Multiple Decisions Ordered Information Tables

Author

Shen, Bin ; Yao, Min ; Wu, Zhaohui

Author_Institution

Coll. of Comput., Zhejiang Univ., Hangzhou

fYear

2008

fDate

15-19 Dec. 2008

Firstpage

573

Lastpage

582

Abstract

Ordered information table is one of the most important research areas of granular computing. In this thesis, we introduce multiple decisions ordered information tables based on the concept of ordered information tables. Multiple decisions ordered information tables are used to describe the actual multiple decision attributes situation of reality. We study the process of rule extraction from multiple decisions ordered information tables thoroughly and several concepts about this process are proposed and discussed. At last, an example of multiple decisions ordered information tables is used to illustrate the basic ideas. These ideas and methods are quite useful for KDD, DM and GC.

Keywords

data mining; decision tables; granular computing; multiple decisions ordered information tables; rules extraction; Association rules; Conferences; Data mining; Delta modulation; Educational institutions; Information retrieval; Machine learning; Rough sets; Technology management; Rules extraction; ordered information table; ordered rule;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Mining Workshops, 2008. ICDMW '08. IEEE International Conference on

Conference_Location

Pisa

Print_ISBN

978-0-7695-3503-6

Electronic_ISBN

978-0-7695-3503-6

Type

conf

DOI

10.1109/ICDMW.2008.75

Filename

4733981