Interpreting the Kohonen self-organizing feature map using contiguity-constrained clustering
Pattern Recognition Letters
Information Retrieval
Self-Organizing Maps
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
A large-scale study of the evolution of web pages
WWW '03 Proceedings of the 12th international conference on World Wide Web
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
On the Depth and Dynamics of Online Search Behavior
Management Science
MONIC: modeling and monitoring cluster transitions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Clustering data stream: A survey of algorithms
International Journal of Knowledge-based and Intelligent Engineering Systems
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The growing number of traces left behind user transactions on the Internet (e.g. customer purchases, user navigations, etc.) has increased the importance of Web usage data analysis. A notable challenge of this analysis is the fact that the way in which a website is visited can evolve over time. As a result, the usage models must be continuously updated in order to reflect the current behaviour of the visitors. In this article, we introduce CAMEUD, a clustering approach to mine and detect changes in evolving usage data. The proposed approach is totally independent from the clustering algorithm applied in the classification problem and is able to detect and determine the nature of changes undergone by the usage groups (appearance, disappearance, fusion and split) at subsequent time intervals. Experiments on synthetic and real usage data sets evaluate the efficiency of CAMEUD.