Interpretable interval type-2 fuzzy predicates for data clustering: A new automatic generation method based on self-organizing maps-Reference-Cited by-同舟云学术

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Interpretable interval type-2 fuzzy predicates for data clustering: A new automatic generation method based on self-organizing maps

Published:2017-10 Issue: Volume:133 Page:234-254
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Comas Diego S.,Pastore Juan I.,Bouchet Agustina,Ballarin Virginia L.,Meschino Gustavo J.

Funder

Consejo Nacional de Investigaciones Científicas y Técnicas

CONICET

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference53 articles.

1. Data clustering: a review;Jain;ACM Comput. Surv,1999

2. Automatic design of interpretable fuzzy predicate systems for clustering using self-organizing maps;Meschino;Neurocomputing,2015

3. Discovering knowledge from data clustering using automatically-defined interval type-2 fuzzy predicates;Comas;Expert Syst. Appl.,2017

4. The brain MR Image segmentation techniques and use of diagnostic packages;Dubey;Acad. Radiol.,2010

5. Robust spatially constrained fuzzy c-means algorithm for brain MR image segmentation;Zexuan;Pattern Recognit.,2014

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