Recent advances in intuitionistic fuzzy information aggregation
Aggregation of intuitionistic fuzzy information is a new branch of intuitionistic fuzzy set
theory, which has attracted significant interest from researchers in recent years. In this paper …
theory, which has attracted significant interest from researchers in recent years. In this paper …
Generalized picture distance measure and applications to picture fuzzy clustering
LH Son - Applied Soft Computing, 2016 - dl.acm.org
Display Omitted We focused on the clustering problem in picture fuzzy sets. A generalized
picture distance measure was proposed. A novel hierarchical picture clustering method …
picture distance measure was proposed. A novel hierarchical picture clustering method …
An improved intuitionistic fuzzy c-means clustering algorithm incorporating local information for brain image segmentation
H Verma, RK Agrawal, A Sharan - Applied Soft Computing, 2016 - Elsevier
The segmentation of brain magnetic resonance (MR) images plays an important role in the
computer-aided diagnosis and clinical research. However, due to presence of noise and …
computer-aided diagnosis and clinical research. However, due to presence of noise and …
[图书][B] Hesitant fuzzy sets theory
Z Xu - 2014 - Springer
When people make a decision, they are usually hesitant and irresolute for one thing or
another which makes it difficult to reach a final agreement. For example, two decision …
another which makes it difficult to reach a final agreement. For example, two decision …
Density-based IFCM along with its interval valued and probabilistic extensions, and a review of intuitionistic fuzzy clustering methods
Fuzzy clustering has been useful in capturing the uncertainty present in the data during
clustering. Most of the c-Means algorithms such as FCM (Fuzzy c-Means), IFCM …
clustering. Most of the c-Means algorithms such as FCM (Fuzzy c-Means), IFCM …
Picture fuzzy clustering: a new computational intelligence method
PH Thong, LH Son - Soft computing, 2016 - Springer
Fuzzy clustering especially fuzzy C C-means (FCM) is considered as a useful tool in the
processes of pattern recognition and knowledge discovery from a database; thus being …
processes of pattern recognition and knowledge discovery from a database; thus being …
A partial binary tree DEA-DA cyclic classification model for decision makers in complex multi-attribute large-group interval-valued intuitionistic fuzzy decision-making …
This paper proposes the idea of combining “interest groups” with the practical decision
information to classify the decision makers (DMs) in complex multi-attribute large-group …
information to classify the decision makers (DMs) in complex multi-attribute large-group …
A Novel Evolutionary Kernel Intuitionistic Fuzzy -means Clustering Algorithm
KP Lin - IEEE Transactions on Fuzzy systems, 2013 - ieeexplore.ieee.org
This study proposes a novel evolutionary kernel intuitionistic fuzzy c-means clustering
algorithm (EKIFCM) that combines Atanassov's intuitionistic fuzzy sets (IFSs) with kernel …
algorithm (EKIFCM) that combines Atanassov's intuitionistic fuzzy sets (IFSs) with kernel …
Sparse representation-based intuitionistic fuzzy clustering approach to find the group intra-relations and group leaders for large-scale decision making
In this paper, a sparse representation-based intuitionistic fuzzy clustering (SRIFC) approach
is presented for solving the large-scale decision making (LSDM) problem. It consists of two …
is presented for solving the large-scale decision making (LSDM) problem. It consists of two …
PIFHC: The probabilistic intuitionistic fuzzy hierarchical clustering algorithm
Hierarchical clustering techniques help in building a tree-like structure called dendrogram
from the data points which can be used to find the closest related data objects. This paper …
from the data points which can be used to find the closest related data objects. This paper …