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EUSFLAT Working Group on Learning and Data Mining
Aims and Scope
Topics in the fields of machine learning and data mining have attracted considerable attention within the fuzzy set community in recent years. There are several motivations for combining tools and techniques from fuzzy set theory with learning and data mining methods, notably the following: Firstly, learning and adaptivity have become important aspects in fuzzy systems design, where data-driven approaches can complement knowledge-based methods in a reasonable way. Secondly, recent research has shown that fuzzy set theory can contribute to machine learning and data mining in a substantial way, e.g., in dealing with uncertainty in model induction or extracting vague patterns and relationships from data.
The general goal of the working group is to promote research in the field of fuzzy machine learning and data mining. Moreover, the working group shall provide a forum for discussions on this topic and a repository for resources on fuzzy data mining, including, e.g., software and benchmark data sets.
|Name / Affiliation||E-Mail / www|
|Plamen Angelov / Lancaster University, United Kingdomemail@example.com|
|Eyke Hüllermeier / Paderborn University, Germanyfirstname.lastname@example.org|
|Frank Klawonn / University of Applied Sciences Braunschweig/Wolfenbüttel, Germany||F.Klawonn@fh-wolfenbuettel.de|
|Daniel Sánchez / Dept. Computer Science and A.I., University of Granada, Spainemail@example.com|