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Clustering Web Information Services

Clustering Web Information Services

Athena Vakali, George Pallis, Lefteris Angelis
Copyright: © 2007 |Pages: 22
ISBN13: 9781599042282|ISBN10: 1599042282|ISBN13 Softcover: 9781599042299|EISBN13: 9781599042305
DOI: 10.4018/978-1-59904-228-2.ch002
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MLA

Vakali, Athena, et al. "Clustering Web Information Services." Web Data Management Practices: Emerging Techniques and Technologies, edited by Athena Vakali and George Pallis, IGI Global, 2007, pp. 34-55. https://doi.org/10.4018/978-1-59904-228-2.ch002

APA

Vakali, A., Pallis, G., & Angelis, L. (2007). Clustering Web Information Services. In A. Vakali & G. Pallis (Eds.), Web Data Management Practices: Emerging Techniques and Technologies (pp. 34-55). IGI Global. https://doi.org/10.4018/978-1-59904-228-2.ch002

Chicago

Vakali, Athena, George Pallis, and Lefteris Angelis. "Clustering Web Information Services." In Web Data Management Practices: Emerging Techniques and Technologies, edited by Athena Vakali and George Pallis, 34-55. Hershey, PA: IGI Global, 2007. https://doi.org/10.4018/978-1-59904-228-2.ch002

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Abstract

The explosive growth of the Web scale has drastically increased information circulation and dissemination rates. As the number of both Web users and Web sources grows significantly everyday, crucial data management issues, such as clustering on the Web, should be addressed and analyzed. Clustering has been proposed towards improving both the information availability and the Web users’ personalization. Clusters on the Web are either users’ sessions or Web information sources, which are managed in a variation of applications and implementations testbeds. This chapter focuses on the topic of clustering information over the Web, in an effort to overview and survey on the theoretical background and the adopted practices of most popular emerging and challenging clustering research efforts. An up-to-date survey of the existing clustering schemes is given, to be of use for both researchers and practitioners interested in the area of Web data mining.

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