Automatic personalization based on Web usage mining
Communications of the ACM
Recovering software requirements from system-user interaction traces
SEKE '02 Proceedings of the 14th international conference on Software engineering and knowledge engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
From run-time behavior to usage scenarios: an interaction-pattern mining approach
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Industry: predicting telecommunication equipment failures from sequences of network alarms
Handbook of data mining and knowledge discovery
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Web Mining: Information and Pattern Discovery on the World Wide Web
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Statistical testing of web applications
Journal of Software Maintenance and Evolution: Research and Practice - Special issue: Web site evolution
Mining interesting knowledge from weblogs: a survey
Data & Knowledge Engineering
A framework for representing navigational patterns as full temporal objects
ACM SIGecom Exchanges
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Every day, new information, products and services are being offered by providers on the World Wide Web. At the same time, the number of consumers and the diversity of their interests increase. As a result, providers are seeking ways to infer the customers' interests and to adapt their web sites to make the content of interest more easily accessible. Pattern mining is a promising approach in support of this goal. Assuming that past navigation behavior is an indicator of the users' interests, then, the records of this behavior, kept in the form of the web-server logs, can be mined to infer what the users are interested in. On that basis, recommendations can be dynamically generated, to help new web-site visitors find the information of interest faster. In this paper, we discuss our experience with pattern mining for dynamic web-site adaptation. Our particular approach is tailored to "focused" web sites that offer information on a well-defined subject, such as, for example, the web site of an undergraduate course. Visitors of such focused sites exhibit similar types of navigation behavior, corresponding to the services offered by the web site; therefore, page recommendation based on usage-pattern mining can be quite effective.