[A Ba13] Parallel architectures for fuzzy triadic similarity learning

Conférence Internationale avec comité de lecture : International Conference on Control, Engineering & Information Technology (CEIT), December 2013, Vol. 1(Proceedings Engineering & Technology), pp.121-126, Series CoRR, 2013, (DOI:
Résumé: In a context of document co-clustering, we define a new similarity measure which iteratively computes similarity while combining fuzzy sets in a three-partite graph. The fuzzy triadic similarity (FT-Sim) model can deal with uncertainty offers by the fuzzy sets. Moreover, with the development of the Web and the high availability of storage spaces, more and more documents become accessible. Documents can be provided from multiple sites and make similarity computation an expensive processing. This problem motivated us to use parallel computing. In this paper, we introduce parallel architectures which are able to treat large and multi-source data sets by a sequential, a merging or a splitting-based process. Then, we proceed to a local and a central (or global) computing using the basic FT-Sim measure. The idea behind these architectures is to reduce both time and space complexities thanks to parallel computation.


Equipe: sys
Collaboration: ENIT


@inproceedings {
A Ba13,
title="{Parallel architectures for fuzzy triadic similarity learning}",
author=" S. Alouane-Ksouri and M. Sassi Hidri and K. Barkaoui ",
booktitle="{International Conference on Control, Engineering & Information Technology (CEIT)}",
series="CoRR, 2013",
issue=Proceedings Engineering & Technology,