LFS 2013 - Special Session on Learning From Static And Dynamic Data With Fuzzy Techniques

Organizers: Abdelhamid Bouchachia, Edwin Lughofer, Daniel Sanchez, Moamar Sayed-Mouchaweh
The special session on Learning from Static and Dynamic Data with Fuzzy Techniques to be held as part of the EUSFLAT’2013 conference in Milano focusses on new methods, algorithms and challenges related to data analysis and modeling.
Specifically the special session surveys the recent advances in model extraction and mining of patterns and knowledge from (static) data using fuzzy methodologies (fuzzy logic, fuzzy sets and fuzzy systems) in connection with machine learning approaches. A special emphasis will be placed on learning methods for data streams that enable incremental/online learning of the model’s parameters, evolving structure, and adaptive process of knowledge induction to deal with the (non-stationary) dynamics of data. The relevance of these methods in real-world applications has been illustrated in various areas such as web mining, Big Data, dynamic social networks, sensor networks, industrial process monitoring, etc.