ASSOCIATIVE TEXT CLASSIFICATION FOR ONLINE DATABASE

Text categorization is regaining interest with the prevalence of digital documents and the wide use of e- mail and web documents, and is becoming a central problem in digital text collections. There have been many approaches to solve this problem, mainly from the machine learning community. This paper explores the use of association rule mining in building a text categorizer. This approach has the advantage of a very fast training phase, less memory usage and the rules of the classifier generated are easy to understand. The investigation leads to conclude that association rule mining is a good and promising strategy for efficient automatic text categorization.