BulDML at Institute of Mathematics and Informatics >
International Journal ITA >
2003 >
Volume 10 Number 3 >

Please use this identifier to cite or link to this item:

Title: Data Mining for Browsing Patterns in Weblog Data by Art Neural Networks
Authors: Nachev, Anatoli
Ganchev, Ivan
Keywords: Data Mining
Neural Networks
Adaptive Resonance Theory
Issue Date: 2003
Publisher: Institute of Information Theories and Applications FOI ITHEA
Abstract: Categorising visitors based on their interaction with a website is a key problem in Web content usage. The clickstreams generated by various users often follow distinct patterns, the knowledge of which may help in providing customised content. This paper proposes an approach to clustering weblog data, based on ART2 neural networks. Due to the characteristics of the ART2 neural network model, the proposed approach can be used for unsupervised and self-learning data mining, which makes it adaptable to dynamically changing websites.
ISSN: 1313-0463
Appears in Collections:Volume 10 Number 3

Files in This Item:

File Description SizeFormat
ijita10-3-p19.pdf81.19 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


Valid XHTML 1.0!   Creative Commons License