Participatory Learning Fuzzy Clustering for Interval-Valued Data

2016 ◽  
pp. 687-698 ◽  
Author(s):  
Leandro Maciel ◽  
Rosangela Ballini ◽  
Fernando Gomide ◽  
Ronald R. Yager
Keyword(s):  
Fuzzy Clustering ◽  
Participatory Learning ◽  
Interval Valued

Fuzzy clustering of interval-valued data with City-Block and Hausdorff distances

2017 ◽  
Vol 266 ◽  
pp. 659-673 ◽  
Author(s):  
Francisco de A.T. de Carvalho ◽  
Eduardo C. Simões
Keyword(s):  
Fuzzy Clustering ◽  
City Block ◽  
Interval Valued

Fuzzy Clustering with Participatory Learning and Applications

2007 ◽  
pp. 137-153 ◽  
Author(s):  
Leila Roling Scariot da Silva ◽  
Fernando Gomide ◽  
Ronald Yager
Keyword(s):  
Fuzzy Clustering ◽  
Participatory Learning

scholarly journals A Fuzzy Clustering Model for Fuzzy Data with Outliers

2011 ◽  
Vol 1 (2) ◽  
pp. 29-42 ◽  
Author(s):  
M. H. Fazel Zarandi ◽  
Zahra S. Razaee
Keyword(s):  
Fuzzy Clustering ◽  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Wasserstein Distance ◽  
Fuzzy Data ◽  
Simulation Experiments ◽  
Clustering Model ◽  
Interval Valued

This paper proposes a fuzzy clustering model for fuzzy data with outliers. The model is based on Wasserstein distance between interval valued data, which is generalized to fuzzy data. In addition, Keller’s approach is used to identify outliers and reduce their influences. The authors also define a transformation to change the distance to the Euclidean distance. With the help of this approach, the problem of fuzzy clustering of fuzzy data is reduced to fuzzy clustering of crisp data. In order to show the performance of the proposed clustering algorithm, two simulation experiments are discussed.

Towards interval-valued fuzzy set-based collaborative fuzzy clustering algorithms

2018 ◽  
Vol 81 ◽  
pp. 404-416 ◽  
Author(s):  
Long Thanh Ngo ◽  
Trong Hop Dang ◽  
Witold Pedrycz
Keyword(s):  
Fuzzy Clustering ◽  
Fuzzy Set ◽  
Clustering Algorithms ◽  
Interval Valued
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