Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
Abstract
In this study, a biclustering algorithm based query model is proposed that is able to extract biclusters of web objects (i.e., users and pages) from web usage datasets. This Query Based Biclustering (QBB) algorithm is applied to the web usage data to recruit biclusters with respect to query which contain a certain users of similar browsing pattern across a subset of pages of a web site. By this way, one can target the right group of user for the focalized marketing strategy. In this application, the main goal is to identify group of web users or customers with similar behavior so that one can predict the customer’s interest and make proper recommendations to improve their performance. To evaluate the efficiency of the proposed algorithm, the experiment is conducted on the CTI dataset. Results show that the proposed QBB algorithm is efficient in extracting the maximum similar bicluster based on the query.
How to cite this article:
K. Thangavel and R. Rathipriya. Query Based Biclustering of Web Usage Data.
DOI: https://doi.org/10.36478/jeasci.2012.456.461
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2012.456.461