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....
This paper presents an incremental and scalable learning algorithm in order to mine numeric, low dimensionality, high–cardinality, time–changing data streams. Within the Superv...
Mining data streams of changing class distributions is important for real-time business decision support. The stream classifier must evolve to reflect the current class distributi...
Haixun Wang, Jian Yin, Jian Pei, Philip S. Yu, Jef...
Supporting continuous mining queries on data streams requires algorithms that (i) are fast, (ii) make light demands on memory resources, and (iii) are easily to adapt to concept dr...
An adaptive boosting ensemble algorithm for classifying homogeneous distributed data streams is presented. The method builds an ensemble of classifiers by using Genetic Programmi...
Gianluigi Folino, Clara Pizzuti, Giandomenico Spez...