Results 351 to 360 of about 948,152 (383)

Fuzzy C-Means Based DNA Motif Discovery

2008
In this paper, we examined the problem of identifying motifs in DNA sequences. Transcription-binding sites, which are functionally significant subsequences, are considered as motifs. In order to reveal such DNA motifs, our method makes use of Fuzzy clustering of Position Weight Matrix.
Karabulut M., Ibrikci T.
openaire   +1 more source

Deviation-Sparse Fuzzy C-Means With Neighbor Information Constraint

IEEE transactions on fuzzy systems, 2019
This paper introduces sparsity in the traditional fuzzy clustering framework and presents two novel clustering methods. The first one is called deviation-sparse fuzzy c-means (DSFCM). When spatial correlation is encountered, the second method is proposed,
Yuxuan Zhang, X. Bai, Ruirui Fan, Zihan Wang   +3 more
semanticscholar   +1 more source

Rough C-means and Fuzzy Rough C-means for Colour Quantisation

Fundamenta Informaticae, 2012
Colour quantisation algorithms are essential for displaying true colour images using a limited palette of distinct colours. The choice of a good colour palette is crucial as it directly determines the quality of the resulting image. Colour quantisation can also be seen as a clustering problem where the task is to identify those clusters that best ...
Schaefer, G., Hu, Q., Zhou, Huiyu, Peters, J.F., Hassanien, A.E.   +4 more
openaire   +2 more sources

Missing value imputation using a novel grey based fuzzy c-means, mutual information based feature selection, and regression model

Expert systems with applications, 2019
The presence of missing values in real-world data is not only a prevalent problem but also an inevitable one. Therefore, missing values should be handled carefully before the mining or learning process.
A. M. Sefidian, N. Daneshpour
semanticscholar   +1 more source

An optimal hybrid multiclass SVM for plant leaf disease detection using spatial Fuzzy C-Means model

Expert systems with applications, 2022
Santosh Kumar Sahu, Manish Pandey
semanticscholar   +1 more source

Generalizations of Fuzzy c-Means and Fuzzy Classifiers

2016
Different methods of generalized fuzzy c-means having cluster size variables and cluster covariance variables are compared, which include Gustafson-Kessel’s method, Ichihashi’s method of KL-information, and Yang’s method of fuzzified maximum likelihood.
Sadaaki Miyamoto, Yoshiyuki Komazaki, Yasunori Endo   +2 more
openaire   +1 more source

A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data

IEEE Transactions on Medical Imaging, 2002
Mohamed N. Ahmed, S. Yamany, N. Mohamed, A. Farag, T. Moriarty   +4 more
semanticscholar   +1 more source

Comparative analysis of pulmonary nodules segmentation using multiscale residual U-Net and fuzzy C-means clustering

Comput. Methods Programs Biomed., 2021
Jianshe Shi, Yuguang Ye, Daxin Zhu, Lianta Su, Yifeng Huang, Jianlong Huang   +5 more
semanticscholar   +1 more source

Collaborative feature-weighted multi-view fuzzy c-means clustering

Pattern Recognition, 2021
Miin-Shen Yang, Kristina P. Sinaga
semanticscholar   +1 more source

A New Membership Scaling Fuzzy C-Means Clustering Algorithm

IEEE transactions on fuzzy systems, 2021
Shuisheng Zhou, Dong Li, Zhuan Zhang, Ruiyue Ping   +3 more
semanticscholar   +1 more source