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Robust Jointly Sparse Fuzzy Clustering With Neighborhood Structure Preservation
IEEE transactions on fuzzy systems, 2022Fuzzy clustering techniques, especially fuzzy C-means (FCM) and its weighted variants, are typical partitive clustering models that are widely used for revealing possible hidden structures in data.
Jie Zhou +5 more
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Fast Fuzzy Clustering Based on Anchor Graph
IEEE transactions on fuzzy systems, 2021Fuzzy clustering is one of the most popular clustering approaches and has attracted considerable attention in many fields. However, high computational cost has become a bottleneck which limits its applications in large-scale problems.
F. Nie +4 more
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IEEE transactions on fuzzy systems, 2023
Performing an accurate clustering analysis is of great significance for us to understand the behavior of complex networks, and a variety of graph clustering algorithms have, thus, been proposed to do so by taking into account network topology and node ...
Lun Hu +4 more
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Performing an accurate clustering analysis is of great significance for us to understand the behavior of complex networks, and a variety of graph clustering algorithms have, thus, been proposed to do so by taking into account network topology and node ...
Lun Hu +4 more
semanticscholar +1 more source
Kernelized Mahalanobis Distance for Fuzzy Clustering
IEEE transactions on fuzzy systems, 2021Data samples of complicated geometry and nonlinear separability are considered as common challenges to clustering algorithms. In this article, we first construct Mahalanobis distance in the kernel space and then propose a novel fuzzy clustering model ...
Shan Zeng +5 more
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SFCM: A Fuzzy Clustering Algorithm of Extracting the Shape Information of Data
IEEE transactions on fuzzy systems, 2021Topological data analysis is a new theoretical trend using topological techniques to mine data. This approach helps determine topological data structures.
Quang-Thinh Bui +6 more
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Expert systems with applications, 2021
Clustering algorithms aim at finding dense regions of data based on similarities and dissimilarities of data points. Noise and outliers contribute to the computational procedure of the algorithms as well as the actual data points that leads to inaccurate
S. Askari
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Clustering algorithms aim at finding dense regions of data based on similarities and dissimilarities of data points. Noise and outliers contribute to the computational procedure of the algorithms as well as the actual data points that leads to inaccurate
S. Askari
semanticscholar +1 more source
Quality and Reliability Engineering International, 2010
AbstractGrouping unknown data into groups of similar data is a necessary first step for classification, indexing of databases, and prediction. Most of the current applications, such as news classification, blog indexing, image classification, and medical diagnosis, obtain their data in temporal sequence or online.
Petra Perner, Anja Attig
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AbstractGrouping unknown data into groups of similar data is a necessary first step for classification, indexing of databases, and prediction. Most of the current applications, such as news classification, blog indexing, image classification, and medical diagnosis, obtain their data in temporal sequence or online.
Petra Perner, Anja Attig
openaire +1 more source
Biocybernetics and Biomedical Engineering, 2020
In medical image processing, brain tumor detection and segmentation is a challenging and time-consuming task. Magnetic Resonance Image (MRI) scan analysis is a powerful tool in the recent technology that makes effective detection of the abnormal tissues ...
P. Raja, A. Rani
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In medical image processing, brain tumor detection and segmentation is a challenging and time-consuming task. Magnetic Resonance Image (MRI) scan analysis is a powerful tool in the recent technology that makes effective detection of the abnormal tissues ...
P. Raja, A. Rani
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Deep Fuzzy Clustering—A Representation Learning Approach
IEEE transactions on fuzzy systems, 2020Fuzzy clustering is a classical approach to provide the soft partition of data. Although its enhancements have been intensively explored, fuzzy clustering still suffers from the difficulties in handling real high-dimensional data with complex latent ...
Qiying Feng +3 more
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IEEE transactions on fuzzy systems, 2022
Fuzzy $c$-means (FCM) clustering is a promising method to handle uncertainties in data clustering. However, the traditional FCM and most of its variants cannot address incomplete inputs.
Yan Song +4 more
semanticscholar +1 more source
Fuzzy $c$-means (FCM) clustering is a promising method to handle uncertainties in data clustering. However, the traditional FCM and most of its variants cannot address incomplete inputs.
Yan Song +4 more
semanticscholar +1 more source