EUSFLAT 2017 Special Session

Algorithms for Data Aggregation and Fusion

About the EUSFLAT 2017 Conference

10th Conference of the European Society for Fuzzy Logic and Technology – EUSFLAT 2017

September 11-15, 2017, Warsaw, Poland

The aim of the conference is to bring together researchers dealing with the theory and applications of computational intelligence, fuzzy logic, fuzzy systems, soft computing and related areas. The conferrence, as its predecessors, will provide a platform for the exchange of ideas among scientists, engineers and students.

All accepted papers will be published in a volume of the Springer's Advances in Intelligent Systems and Computing series, and submitted for indexation in the Thomson Reuters Conference Proceedings Citation Index-Science (CPCI-S). Please visit this series homepage for instructions on how to prepare the paper.

We would like to invite the researchers to submit their contributions to the special session entitled Algorithms for Data Aggregation and Fusion.

Description of the Special Session

Aggregation theory by default deals with mathematical aspects of diverse ways to summarize homogeneous data. This includes the construction and analysis of functions that act on elements in the real line (means, t-norms, t-conorms, fuzzy implications) as well as other lattices.

This session is focused on algorithmic aspects of data aggregation and fusion, including big, computationally demanding, complex and/or heterogeneous data sets. Non-standard and non-classical approaches to aggregation are especially welcome.


Topics include, but are not limited to:
  • complex data aggregation and fusion:
    • multidimensional real vectors
    • rankings
    • character strings (DNA sequences, etc.)
    • directional and spatial data
    • trees and other types of graphs
    • intervals and fuzzy quantities
    • time series
  • big data problems
  • algorithmic aspects of classical aggregation functions
  • learning aggregation functions from data
  • record linkage between heterogeneous data sets
  • applications:
    • bioinformatics
    • decision making
    • data analysis
    • classification
    • clustering
    • computational statistics
    • bibliometrics
    • economics
    • pattern recognition
    • plagiarism detection
    • automated spelling correction


Please feel free to send any questions or comments to the organizers.
  • Bernard De Baets
    KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University
    Ghent, Belgium
  • Marek Gagolewski
    Systems Research Institute, Polish Academy of Sciences
    Warsaw, Poland
  • Simon James
    School of Information Technology
    Melbourne, Australia