An efficient approach for Web-log mining using ART

Information on the Web is growing dramatically. Without a recommended system, the users may spend lots of time on the Web in finding the information they are interested in [5]. With the Web becoming the most popular media for collecting, sharing, and distributing information, it is very common for educational institutions, and organizations to develop Web-Based Training (WBT) systems [6]. Data mining in Web log known as Web-log mining or Web mining has been a hot spot of research work. Many Web mining methods based on association rule [1] have been proposed. Data on the Web is really unstructured, and implementation of association rule has some limitation. Overcome of these limitation can be done with neuro-fuzzy approach but without optimization. In this paper, we present a novel technique for Web-log mining using ART (Adaptive Resonance Network), and compare it with neuro-fuzzy approach.