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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.

Coordinators

Name/Affiliation E-Mail/WWW
Plamen Angelov
Lancaster University, United Kingdom

http://www.lancs.ac.uk/staff/angelov/
Eyke Hüllermeier
University of Marburg, Germany

http://www.mathematik.uni-marburg.de/~eyke
Frank Klawonn
University of Applied Sciences Braunschweig/Wolfenbüttel, Germany

http://public.rz.fh-wolfenbuettel.de/~klawonn/
Daniel Sánchez
European Centre for Soft Computing, Mieres, Asturias, Spain
Dept. Computer Science and A.I., University of Granada, Spain

http://www.softcomputing.es

http://decsai.ugr.es/~daniel

Contact and Further Information

E-Mail:

Homepage of working group:
http://idbis.ugr.es/dami/