Title :
A new associative classifier for text categorization
Author :
Su, Zhitong ; Song, Wei ; Meng, Dan ; Li, Jinhong
Author_Institution :
Coll. of Inf. Eng., North China Univ. of Technol., Beijing, China
Abstract :
Text categorization has become one of the key techniques for handling and organizing text data. In practical text classification tasks, the ability to interpret the classification result is as important as the ability to classify exactly. Associative classifiers have many favorable characteristics such as rapid training, good classification accuracy, and excellent interpretation. In this paper, Closed-AC, which is a new associative classifier for text categorization, is proposed. Firstly, rough set is used to dimension reduction. Then, only generic rules composed of closed itemsets are used for classification. Experimental results show benefits of the proposed associative classifier.
Keywords :
associative processing; classification; data reduction; rough set theory; text analysis; associative classifier; dimension reduction; generic rule; rough set theory; text categorization; text classification; text data handling; Association rules; Data engineering; Data mining; Databases; Educational institutions; Electronic mail; Intelligent systems; Itemsets; Knowledge engineering; Text categorization;
Conference_Titel :
Intelligent System and Knowledge Engineering, 2008. ISKE 2008. 3rd International Conference on
Conference_Location :
Xiamen
Print_ISBN :
978-1-4244-2196-1
Electronic_ISBN :
978-1-4244-2197-8
DOI :
10.1109/ISKE.2008.4730943