ACFC: Ant Colony with Fuzzy Clustering Algorithm for Community Detection in Social Networks

2017 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Marjan Naderan ◽  
Marjan Naderan ◽  
Seyed Enayatollah Alavi
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Ant Colony ◽  
Fuzzy Clustering Algorithm

ACFC: ant colony with fuzzy clustering algorithm for community detection in social networks

2019 ◽  
Vol 31 (1) ◽  
pp. 36 ◽  
Author(s):  
Ehsan Noveiri ◽  
Marjan Naderan ◽  
Seyed Enayatollah Alavi
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Ant Colony ◽  
Fuzzy Clustering Algorithm

Fuzzy clustering algorithm based on w-mean distance

2013 ◽  
Vol 32 (7) ◽  
pp. 1978-1982
Author(s):  
Rui-li ZHANG ◽  
Ji-fu ZHANG
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Fuzzy Clustering Algorithm

Transfer of knowledge in chemical equipment reliability

1989 ◽  
Vol 54 (10) ◽  
pp. 2692-2710 ◽  
Author(s):  
František Babinec ◽  
Mirko Dohnal
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Chemical Equipment ◽  
Transfer Of Knowledge ◽  
Equipment Reliability ◽  
Fuzzy Clustering Algorithm

The problem of transformation of data on the reliability of chemical equipment obtained in particular conditions to other equipment in other conditions is treated. A fuzzy clustering algorithm is defined for this problem. The method is illustrated on a case study.

Segmentation of corpus callosum based on tensor fuzzy clustering algorithm

2021 ◽  
pp. 1-14
Author(s):  
Yujia Qu ◽  
Yuanjun Wang
Keyword(s):  
Corpus Callosum ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Diffusion Tensor ◽  
Region Of Interest ◽  
Midsagittal Plane ◽  
Fuzzy Clustering Algorithm ◽  
Human Connectome Project ◽  
Clustering Center ◽  
Fixed Region

BACKGROUND: The corpus callosum in the midsagittal plane plays a crucial role in the early diagnosis of diseases. When the anisotropy of the diffusion tensor in the midsagittal plane is calculated, the anisotropy of corpus callosum is close to that of the fornix, which leads to blurred boundary of the segmentation region. OBJECTIVE: To apply a fuzzy clustering algorithm combined with new spatial information to achieve accurate segmentation of the corpus callosum in the midsagittal plane in diffusion tensor images. METHODS: In this algorithm, a fixed region of interest is selected from the midsagittal plane, and the anisotropic filtering algorithm based on tensor is implemented by replacing the gradient direction of the structural tensor with an eigenvector, thus filtering the diffusion tensor of region of interest. Then, the iterative clustering center based on K-means clustering is used as the initial clustering center of tensor fuzzy clustering algorithm. Taking filtered diffusion tensor as input data and different metrics as similarity measures, the neighborhood diffusion tensor pixel calculation method of Log Euclidean framework is introduced in the membership function calculation, and tensor fuzzy clustering algorithm is proposed. In this study, MGH35 data from the Human Connectome Project (HCP) are tested and the variance, accuracy and specificity of the experimental results are discussed. RESULTS: Segmentation results of three groups of subjects in MGH35 data are reported. The average segmentation accuracy is 97.34%, and the average specificity is 98.43%. CONCLUSIONS: When segmenting the corpus callosum of diffusion tensor imaging, our method cannot only effective denoise images, but also achieve high accuracy and specificity.

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