@inproceedings{ff05a22ccc924ebbac00cb55221b3933,
title = "An efficient approach for Web-log mining using ART",
abstract = "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.",
keywords = "ART (Adaptive Resonance Network), Attention-subsystem, Fast-learning, Neuro-fuzzy, Orienting subsystem, Slow-learning, Web-log, Web-mining",
author = "Shantanu Sharma and Manish Varshney",
year = "2010",
doi = "10.1109/ICEMT.2010.5657673",
language = "English (US)",
isbn = "9781424486175",
series = "ICEMT 2010 - 2010 International Conference on Education and Management Technology, Proceedings",
pages = "196--199",
booktitle = "ICEMT 2010 - 2010 International Conference on Education and Management Technology, Proceedings",
note = "2010 International Conference on Education and Management Technology, ICEMT 2010 ; Conference date: 02-11-2010 Through 04-11-2010",
}