INFORMS Journal on Computing articles from March 2003

INFORMS Journal on Computing archives from March 2003

Special issue on mining Web-based data for e-business applications. (From the Guest Co-Editors).
March 22, 2003... Over a period of few short years, the Web became the most comprehensive source of information ever created. This information is contained not only in HTML and XML pages, but also in Weblog files (also called clickstreams) containing Web page...

Efficient and anonymous Web-usage mining for Web personalization.
March 22, 2003... The World Wide Web (WWW) is the largest distributed information space and has grown to encompass diverse information resources. Although the web is growing exponentially, the individual's capacity to read and digest content is essentially...

On the existence and significance of data preprocessing biases in Web-usage mining.
March 22, 2003... The literature on web-usage mining is replete with data preprocessing techniques, which correspond to many closely related problem formulations. We survey data-preprocessing techniques for session-level pattern discovery and compare three of...

A framework for the evaluation of session reconstruction heuristics in Web-usage analysis.
March 22, 2003... Web-usage mining has become the subject of intensive research, as its potential for personalized services, adaptive Web sites and customer profiling is recognized. However, the reliability of Web-usage mining results depends heavily on the...

Web business intelligence: mining the Web for actionable knowledge.
March 22, 2003... It is estimated that over seven billion static pages exist in the Web today, and backend databases can potentially produce at least three times as many dynamic pages. However, the best search engines index only approximately 20% of the static...

Relationship-based clustering and visualization for high-dimensional data mining.
March 22, 2003... In several real-life data-mining applications, data reside in very high (1000 or more) dimensional space, where both clustering techniques developed for low-dimensional spaces (k-means, BIRCH, CLARANS, CURE, DBScan, etc.) as well as...