An efficient approach for Web-log mining using ART

Author

Sharma, Shantanu ; Varshney, Manish

Author_Institution

Comput. Eng., Nat. Inst. of Technol., Kurukshetra, India

fYear

2010

fDate

2-4 Nov. 2010

Firstpage

196

Lastpage

199

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ICEMT.2010.5657673

Filename

5657673