Results 241 to 250 of about 112,252 (298)

Data inherent assessment of electrical equipment by failure pattern and self-evaluation decision-making algorithm. [PDF]

Sci Rep
Aranizadeh A, Vahidi B, Khorsandi A.
europepmc   +1 more source

FAIR-Net: A Fuzzy Autoencoder and Interpretable Rule-Based Network for Ancient Chinese Character Recognition. [PDF]

Sensors (Basel)
Ge Y, Zhang Y, Roh SB.
europepmc   +1 more source

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Relative entropy fuzzy c-means clustering

Information Sciences, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zarandi, Mohammad Hossein Fazel, Zarinbal, M., Türkşen, İsmail Burhan   +2 more
openaire   +3 more sources

Suppressed fuzzy c-means clustering algorithm

Pattern Recognition Letters, 2003
Summary: Based on the defect of rival checked fuzzy \(c\)-means clustering algorithm, a new algorithm: suppressed fuzzy \(c\)-means clustering algorithm is proposed. The new algorithm overcomes the shortcomings of the original algorithm, establishes more natural and more reasonable relationships between hard \(c\)-means clustering algorithm and fuzzy \(
Fan, Jiu-Lun, Zhen, Wen-Zhi, Xie, Wei-Xin   +2 more
openaire   +2 more sources

Gaussian Collaborative Fuzzy C-Means Clustering

International Journal of Fuzzy Systems, 2021
For most FCM-based fuzzy clustering algorithms, several problems, such as noise, non-spherical clusters, and size-imbalanced clusters, are difficult to solve. Different fuzzy clustering algorithms are developed to deal with these problems from different perspectives. However, no comprehensive viewpoint to generalize these problems has been put forward.
Yunlong Gao, Zhihao Wang, Huidui Li, Jinyan Pan   +3 more
openaire   +1 more source

Fuzzy c-means clustering of incomplete data

IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2001
The problem of clustering a real s-dimensional data set X={x(1 ),,,,,x(n)} subset R(s) is considered. Usually, each observation (or datum) consists of numerical values for all s features (such as height, length, etc.), but sometimes data sets can contain vectors that are missing one or more of the feature values.
R J, Hathaway, J C, Bezdek
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Pythagorean Fuzzy c-means Clustering Algorithm

2021
This article presents algorithm for \(c\)-means clustering under Pythagorean fuzzy environment. In this method Pythagorean fuzzy generator is developed to convert the data points from crisp to Pythagorean fuzzy numbers (PFNs). Subsequently, Euclidean distance is used to measure the distances between data points.
Souvik Gayen, Animesh Biswas
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Robust weighted fuzzy c-means clustering

2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence), 2008
Nowadays, the fuzzy c-means method (FCM) became 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. This paper presents a robust clustering algorithm called robust weighted fuzzy c-means (RWFCM).
A. H. Hadjahmadi, M. M. Homayounpour, S. M. Ahadi   +2 more
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Projected Rough Fuzzy c-means clustering

2011 11th International Conference on Intelligent Systems Design and Applications, 2011
The conventional rough set based feature selection techniques find the relevant features for the entire data set. However different sets of dimensions may be relevant for different clusters. This paper introduces a novel Projected Rough Fuzzy c-means clustering algorithm (PRFCM) which employs rough sets to model uncertainty in data, and fuzzy set ...
Charu Pun, Naveen Kumar
openaire   +1 more source