Title :
Rules Extraction from Multiple Decisions Ordered Information Tables
Author :
Shen, Bin ; Yao, Min ; Wu, Zhaohui
Author_Institution :
Coll. of Comput., Zhejiang Univ., Hangzhou
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;
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
DOI :
10.1109/ICDMW.2008.75
