Title :
Building Accurate Associative Classifier Based on Closed Itemsets and Certainty Factor
Author :
Deng, Zhongjun ; Zheng, Xuefeng ; Song, Wei
Author_Institution :
Sch. of Inf. Eng., Univ. of Sci. & Technol. Beijing, Beijing, China
Abstract :
The application of association rule mining to classification has led to a new family of classifiers which are often referred to as Associative Classifiers (ACs). An advantage of ACs is that they are rule-based and thus lend themselves to an easier interpretation. However, it is common knowledge that association rule mining typically yields a sheer number of rules defeating the purpose of a human readable model. Hence, selecting and ranking a small subset of high-quality rules without jeopardizing the classification accuracy is paramount. This article introduces a new method for building associative classifier. In this method, only association rules based on closed itemsets are used for constructing classifier. Furthermore, certainty factor is used for ranking rules in classifier. Experimental results show that the proposed associative classifier is effective.
Keywords :
data mining; pattern classification; association rule mining; association rules; associative classifier; certainty factor; closed itemsets; high-quality rules; human readable model; Association rules; Data mining; Educational institutions; Expert systems; Humans; Information retrieval; Information technology; Intelligent structures; Itemsets; Training data; associative classification; certainty factor; closed itemset; data mining;
Conference_Titel :
Intelligent Information Technology Application Workshops, 2009. IITAW '09. Third International Symposium on
Conference_Location :
Nanchang
Print_ISBN :
978-1-4244-6420-3
Electronic_ISBN :
978-1-4244-6421-0
DOI :
10.1109/IITAW.2009.24