Title :
An efficient approach for Web-log mining using ART
Author :
Sharma, Shantanu ; Varshney, Manish
Author_Institution :
Comput. Eng., Nat. Inst. of Technol., Kurukshetra, India
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 neural nets; Internet; data mining; fuzzy set theory; recommender systems; Web based training systems; Web log mining; adaptive resonance network; association rule; neuro fuzzy approach; recommended system; Adaptation model; Artificial neural networks; Clustering algorithms; Computer architecture; Subspace constraints; Web mining; ART (Adaptive Resonance Network); Neuro-Fuzzy; Web-Mining; Web-log; attention-subsystem; fast-learning; orienting subsystem; slow-learning;
Conference_Titel :
Education and Management Technology (ICEMT), 2010 International Conference on
Conference_Location :
Cairo
Print_ISBN :
978-1-4244-8616-8
Electronic_ISBN :
978-1-4244-8618-2
DOI :
10.1109/ICEMT.2010.5657673
