DocumentCode :
469330
Title :
A Rough Set Based Associative Classifier
Author :
Rodda, Sireesha ; Shashi, M.
Author_Institution :
GITAM Univ., Visakhapatnam
Volume :
2
fYear :
2007
fDate :
13-15 Dec. 2007
Firstpage :
291
Lastpage :
295
Abstract :
Associative Classification integrates both association rule mining and classification tasks. Many studies show that associative classifiers give better accuracy than other traditional classifiers. Traditional classification techniques such as decision trees and RIPPER use heuristic search methods to perform classification. Associative classification system is more robust and makes predictions based on entire dataset. In this paper, we use rough sets for feature reduction. We have also introduced two new criteria for ranking the association rules. This improves the overall accuracy of the classifier. Our preliminary results with some UCIML datasets are very encouraging.
Keywords :
data mining; pattern classification; rough set theory; association rule classification tasks; association rule mining; associative classification; decision trees; feature reduction; heuristic search methods; rough set based associative classifier; Accuracy; Association rules; Classification tree analysis; Data mining; Decision trees; Educational institutions; Information systems; Itemsets; Rough sets; Terminology;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Conference on Computational Intelligence and Multimedia Applications, 2007. International Conference on
Conference_Location :
Sivakasi, Tamil Nadu
Print_ISBN :
0-7695-3050-8
Type :
conf
DOI :
10.1109/ICCIMA.2007.297
Filename :
4426709
Link To Document :
بازگشت