Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms [PDF]
Advances in Fuzzy Systems, 2015 Intuitionistic fuzzy sets (IFSs) provide mathematical framework based on fuzzy sets to describe vagueness in data. It finds interesting and promising applications in different domains. Here, we develop an intuitionistic fuzzy possibilistic C means (IFPCM)
Arindam Chaudhuri
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A Novel Brain MRI Image Segmentation Method Using an Improved Multi-View Fuzzy c-Means Clustering Algorithm [PDF]
Front Neurosci, 2021 Background: The brain magnetic resonance imaging (MRI) image segmentation method mainly refers to the division of brain tissue, which can be divided into tissue parts such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF).
Lei Hua +4 more
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Differential privacy fuzzy C-means clustering algorithm based on gaussian kernel function. [PDF]
PLoS ONE, 2021 Fuzzy C-means clustering algorithm is one of the typical clustering algorithms in data mining applications. However, due to the sensitive information in the dataset, there is a risk of user privacy being leaked during the clustering process.
Yaling Zhang, Jin Han
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Mixed fuzzy C-means clustering
Information ScienceszbMATH Open Web Interface contents unavailable due to conflicting licenses.
H. Demirhan
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Conditional semi‐fuzzy c‐means clustering for imbalanced dataset
IET Image Processing, 2020 Fuzzy c‐means algorithms have been widely utilised in several areas such as image segmentation, pattern recognition and data mining. However, the related studies showed the limitations in facing imbalanced datasets. The maximum fuzzy boundary tends to be
Yunlong Gao +4 more
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Bilateral Weighted Fuzzy C-Means Clustering
Iranian Journal of Electrical and Electronic Engineering, 2012 Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise.
A. H. Hadjahmadi +2 more
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Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients
Algorithms, 2020 Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering ...
Tran Dinh Khang +3 more
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An automated liver segmentation in liver iron concentration map using fuzzy c-means clustering combined with anatomical landmark data [PDF]
BMC Med Imaging, 2021 Kittichai Wantanajittikul +4 more
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Incremental Beta Distribution Weighted Fuzzy C-Ordered Means Clustering
InformationStreaming data is becoming more and more common in the field of big data and incremental frameworks can address its complexity. The BDFCOM algorithm achieves good results on common form datasets by introducing the ordering mechanism of beta distribution ...
Hengda Wang +3 more
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An improved fuzzy clustering image segmentation algorithm combining spatial information
Xi'an Gongcheng Daxue xuebao, 2021 In order to improve the ability of fuzzy C-means (FCM) clustering algorithm to suppress noise, an improved fuzzy clustering image segmentation algorithm was proposed.
Xudong LIU +4 more
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