A Random Indexing Approach for Web User Clustering and Web Prefetching
2012 (English)In: New Frontiers in Applied Data Mining: PAKDD 2011 International Workshops, Shenzhen, China, May 24-27, 2011, Revised Selected Papers / [ed] Longbing Cao, Joshua Zhexue Huang, James Bailey, Yun Sing Koh, Jun Luo, Springer Berlin/Heidelberg, 2012, 40-52 p.Chapter in book (Refereed)
In this paper we present a novel technique to capture Web users’ behaviour based on their interest-oriented actions. In our approach we utilise the vector space model Random Indexing to identify the latent factors or hidden relationships among Web users’ navigational behaviour. Random Indexing is an incremental vector space technique that allows for continuous Web usage mining. User requests are modelled by Random Indexing for individual users’ navigational pattern clustering and common user profile creation. Clustering Web users’ access patterns may capture common user interests and, in turn, build user profiles for advanced Web applications, such as Web caching and prefetching. We present results from the Web user clustering approach through experiments on a real Web log file with promising results. We also apply our data to a prefetching task and compare that with previous approaches. The results show that Random Indexing provides more accurate prefetchings.
Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2012. 40-52 p.
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 7104
Computer science, Database management, Data mining, Information storage and retrieval systems, Artificial intelligence, Optical pattern recognition, Optical pattern recognition, Pattern Recognition, Pattern Recognition
IdentifiersURN: urn:nbn:se:liu:diva-72860DOI: 10.1007/978-3-642-28320-8_4ISBN: 978-3-642-28319-2ISBN: e-978-3-642-28320-8OAI: oai:DiVA.org:liu-72860DiVA: diva2:463376