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An equidistance index intuitionistic fuzzy c-means clustering algorithm based on local density and membership degree boundary
Fuzzy c-means (FCM) algorithm is an unsupervised clustering algorithm that effectively expresses complex real world information by integrating fuzzy...
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Hybrid fuzzy clustering technique to enhance the performance based on a fusion of intuitionistic modified fuzzy c-means and improved genetic algorithm
In the modern era, there is a sudden rise in data due to the wide use of the web, social networks and so on. Now, it becomes difficult to explore...
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Parameter-Free Auto-Weighted Possibilistic Fuzzy C-Means Clustering with Kernel Metric and Robust Algorithm
To further improve the clustering quality on high dimensional data, this paper makes two improvements of weighted possibilistic fuzzy C-means (WPFCM)...
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Adaptive cuckoo search algorithm based fuzzy C means clustering with random walker algorithm for liver segmentation using CT images
Liver segmentation from computed tomography (CT) images is a significant process for computer-aided diagnosis. Clustering is one of the efficient...
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Brain tumor segmentation based on kernel fuzzy c-means and penguin search optimization algorithm
Brain tumor is the irregular growth of cells in the brain that can develop into malignant or benign tumors. However, the prediction of brain tumors...
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Soybean Genome Clustering Using Quantum-Based Fuzzy C-Means Algorithm
Bioinformatics is a new area of research in which many computer scientists are working to extract some useful information from genome sequences in a...
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Entropy-Based Fuzzy C-Ordered-Means Clustering Algorithm
Fuzzy C -Means is a well-known fuzzy clustering technique. Although FCM can cover the uncertainty problem by forming overlap** clusters, it involves...
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Probabilistic intuitionistic fuzzy c-means algorithm with spatial constraint for human brain MRI segmentation
Segmentation of brain MRI images becomes a challenging task due to spatially distributed noise and uncertainty present between boundaries of soft...
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Derived Multi-population Genetic Algorithm for Adaptive Fuzzy C-Means Clustering
Fuzzy C-Means (FCM) is a common data analysis method, but the clustering effect of this algorithm is easily affected by the initial clustering...
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Performance evaluation of spatial fuzzy C-means clustering algorithm on GPU for image segmentation
Image processing by segmentation technique is an important phase in medical imaging such as MRI. Its objective is to analyze the different tissues in...
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Fuzzy C-Means for image segmentation: challenges and solutions
Image segmentation is considered a pertinent prerequisite for numerous tasks in digital image processing. The procedure through which identical...
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Performance of the K-means and fuzzy C-means algorithms in big data analytics
Nowadays, cloud computing is used by most organizations to utilize cloud resources and services in dealing with big data. Besides, machine learning...
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Road crack detection using pixel classification and intensity-based distinctive fuzzy C-means clustering
Road cracks are quickly becoming one of the world's most serious concerns. It may have an impact on traffic safety and increase the likelihood of...
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Fuzzy c-means clustering algorithm with deformable spatial information for image segmentation
Due to the fuzzy c-means(FCM) clustering algorithm is very sensitive to noise and outliers, the spatial information derived from neighborhood window...
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Local feature driven fuzzy local information C-means clustering with kernel metric for blurred and noisy image segmentation
Kernel fuzzy weighted local information C-means clustering is a widely used robust segmentation algorithm for noisy images. However, it cannot...
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Application of Fuzzy c-Means Clustering Algorithm in Consumer Psychology
In order to increase the sales share, mobile phone manufacturers must understand the needs of consumers. The traditional c-means analysis method is...
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A Weighting Possibilistic Fuzzy C-Means Algorithm for Interval Granularity
Granular clustering is an emerging branch in the field of clustering. However, the existing granular clustering algorithms are still immature in...
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Possibilistic picture fuzzy product partition C-means clustering incorporating rich local information for medical image segmentation
Picture fuzzy C-means clustering is a new computational intelligence method that has more significant potential advantages than fuzzy clustering in...
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Robust joint learning of superpixel generation and superpixel-based image segmentation using fuzzy C-multiple-means clustering
In recent years, many superpixel-based image segmentation algorithms have been presented. However, most of these algorithms face issues of high model...
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Integrating fuzzy C-means clustering and fuzzy inference system for audiovisual quality of experience
Estimating the perceived quality and quality of experience of audio-visual signals is critical for several multimedia networks and audio-visual...
