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

• Authors: Olfa Nasraoui;Carlos Rojas;Cesar Cardona
• Venue: Computer Networks: The International Journal of Computer and Telecommunications Networking - Web dynamics
• Year: 2006

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 scenario. We propose a simple similarity measure that has the advantage of explicitly coupling the precision and coverage criteria to the early learning stages. Even though the cosine similarity, and its close relative such as the Jaccard measure, have been prevalent in the majority of Web data clustering approaches, they may fail to explicitly seek profiles that achieve high coverage and high precision simultaneously. We also formulate a validation strategy and adapt several metrics rooted in information retrieval to the challenging task of validating a learned stream synopsis in dynamic environments. Our experiments confirm that the performance of the MinPC similarity is generally better than the cosine similarity, and that this outperformance can be expected to be more pronounced for data sets that are more challenging in terms of the amount of noise and/or overlap, and in terms of the level of change in the underlying profiles/topics (known sub-categories of the input data) as the input stream unravels. In our simulations, we study the task of mining and tracking trends and profiles in evolving text and Web usage data streams in a single pass, and under different trend sequencing scenarios.