A framework for mining evolving trends in Web data streams using dynamic learning and retrospective validation

The expanding and dynamic nature of the Web poses enormous challenges to most data mining techniques that try to extract patterns from Web data, such as Web usage and Web content. While scalable data mining methods are expected to cope with the size challenge, coping with evolving trends in noisy data in a continuous fashion, and without any unnecessary stoppages and reconfigurations is still an open challenge. This dynamic and single pass setting can be cast within the framework of mining evolving data streams. The harsh restrictions imposed by the ``you only get to see it once'' constraint on stream data calls for different computational models that may furthermore bring some interesting surprises when it comes to the behavior of some well known similarity measures during clustering, and even validation. In this paper, we study the effect of similarity measures on the mining process and on the interpretation of the mined patterns in the harsh single pass requirement scenar...

• Type: Journal
• Year: 2006
• Where: CN
• Authors: Olfa Nasraoui, Carlos Rojas, Cesar Cardona
• Keywords: CN 2006, Similarity Measure, Single Pass, Web Data