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- Publisher Website: 10.1007/978-3-540-28651-6_83
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Conference Paper: A clustering model for mining evolving web user patterns in data stream environment
Title | A clustering model for mining evolving web user patterns in data stream environment |
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Authors | |
Issue Date | 2004 |
Publisher | Springer. |
Citation | 5th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2004), Exeter, UK, 25-27 August 2004. In Intelligent Data Engineering and Automated Learning – IDEAL 2004: 5th International Conference, Exeter, UK. August 25-27, 2004: Proceedings, 2004, p. 565-571 How to Cite? |
Abstract | With the fast growing of the Internet and its Web users all over the world, how to manage and discover useful patterns from tremendous and evolving Web information sources become new challenges to our data engineering researchers. Also, there is a great demand on designing scalable and flexible data mining algorithms for various time-critical and data-intensive Web applications. In this paper, we purpose a new clustering model for generating and maintaining clusters efficiently which represent the changing Web user patterns in Websites. With effective pruning process, the clusters can be fast discovered and updated to reflect the current or changing user patterns to Website administrators. This model can also be employed in different Web applications such as personalization and recommendation systems. © Springer-Verlag Berlin Heidelberg 2004. |
Persistent Identifier | http://hdl.handle.net/10722/276802 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
Series/Report no. | Lecture Notes in Computer Science ; 3177 |
DC Field | Value | Language |
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dc.contributor.author | Wu, Edmond H. | - |
dc.contributor.author | Ng, Michael K. | - |
dc.contributor.author | Yip, Andy M. | - |
dc.contributor.author | Chan, Tony F. | - |
dc.date.accessioned | 2019-09-18T08:34:42Z | - |
dc.date.available | 2019-09-18T08:34:42Z | - |
dc.date.issued | 2004 | - |
dc.identifier.citation | 5th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2004), Exeter, UK, 25-27 August 2004. In Intelligent Data Engineering and Automated Learning – IDEAL 2004: 5th International Conference, Exeter, UK. August 25-27, 2004: Proceedings, 2004, p. 565-571 | - |
dc.identifier.isbn | 9783540228813 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276802 | - |
dc.description.abstract | With the fast growing of the Internet and its Web users all over the world, how to manage and discover useful patterns from tremendous and evolving Web information sources become new challenges to our data engineering researchers. Also, there is a great demand on designing scalable and flexible data mining algorithms for various time-critical and data-intensive Web applications. In this paper, we purpose a new clustering model for generating and maintaining clusters efficiently which represent the changing Web user patterns in Websites. With effective pruning process, the clusters can be fast discovered and updated to reflect the current or changing user patterns to Website administrators. This model can also be employed in different Web applications such as personalization and recommendation systems. © Springer-Verlag Berlin Heidelberg 2004. | - |
dc.language | eng | - |
dc.publisher | Springer. | - |
dc.relation.ispartof | Intelligent Data Engineering and Automated Learning – IDEAL 2004: 5th International Conference, Exeter, UK. August 25-27, 2004: Proceedings | - |
dc.relation.ispartofseries | Lecture Notes in Computer Science ; 3177 | - |
dc.title | A clustering model for mining evolving web user patterns in data stream environment | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-540-28651-6_83 | - |
dc.identifier.scopus | eid_2-s2.0-33947127397 | - |
dc.identifier.spage | 565 | - |
dc.identifier.epage | 571 | - |
dc.identifier.eissn | 1611-3349 | - |
dc.publisher.place | Berlin | - |
dc.identifier.issnl | 0302-9743 | - |