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Web log data clustering for a multi-agent recommendation system

Xavier, João C. and Freitas, Alex A. and Canuto, Anne M.P. and Gonçalves, Luis M.G. (2010) Web log data clustering for a multi-agent recommendation system. In: 2010 International Conference on Machine Learning and Cybernetics. IEEE, pp. 182-196. ISBN 978-1-4244-6526-2. E-ISBN 978-1-4244-6527-9. (doi:10.1109/ICMLC.2010.5581017) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/ICMLC.2010.5581017

Abstract

In this paper, we propose an automatic way of recommending information to be visualized by users. The list of information to be recommended is generated based on the web logs of the users stored by the system in a multi relational database. This system is a web-based multi-agent system which provides geographical information and monitors the actions of the users by generating logs. Frequently, the system joins the relational web log data and runs a clustering algorithm in order to recommend a list of most accessed information up to that moment to registered users who log in the system.

Item Type: Book section
DOI/Identification number: 10.1109/ICMLC.2010.5581017
Uncontrolled keywords: clustering algorithms; indexes; sea measurements; monitoring; data mining; geographic information systems; relational databases
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 21 Sep 2012 09:49 UTC
Last Modified: 04 Feb 2020 04:04 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/30653 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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