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Optimizing kernel possibilistic fuzzy C-means clustering using metaheuristic algorithms
Over the past decade, metaheuristic algorithms have gained significant attention from researchers due to their effectiveness and computational...
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Hybrid multi-objective metaheuristic and possibilistic intuitionistic fuzzy c-means algorithms for cluster analysis
This study proposes a hybrid multi-objective meta-heuristics and possibilistic intuitionistic fuzzy c -means (PIFCM) algorithms for cluster analysis....
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Sparse Weighted Multi-view Possibilistic C-Means Clustering with L1 Regularization
Sparse clustering algorithms create clusters with a sparsity of features and are effectively applied to extremely high-dimensional single-view...
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LIPFCM: Linear Interpolation-Based Possibilistic Fuzzy C-Means Clustering Imputation Method for Handling Incomplete Data
Dealing with missing values has been a major obstacle in machine learning. The occurrence of missing data is a significant problem that often results...
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MRI Breast Tumor Extraction Using Possibilistic C Means and Classification Using Convolutional Neural Network
Breast cancer is the most leading cancer disease which demolishes many women lives for the past few decades. It can be prevented and reduce the death...
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Robust Clustering Based Possibilistic Type-2 Fuzzy C-means for Noisy Datasets
Nowadays, clustering dataset is becoming a challenging task due to the complexity of modern datasets including noisy data, outliers, and uncertain...
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An application of sine cosine algorithm-based fuzzy possibilistic c-ordered means algorithm to cluster analysis
Due to advances in information technology, data collection is becoming much easier. Clustering is an important technique for exploring data...
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An Optimized Possibilistic C-means Clustering Image Segmentation Algorithm Based on Spatial Neighborhood Information
The spatial neighborhood information is very important for rejecting the influence of noise. But for most clustering algorithms, there is no...
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Application of computing in recognition of input design factors for vapour-grown carbon nanofibers through fuzzy cluster analysis
The present investigation employed information mining and knowledge learning processes to showcase their efficacy in comprehending the viscoelastic...
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Role of Individual Samples in Modified Possibilistic c-Means Classifier for Handling Heterogeneity Within Mustard Crop
In raster remote sensing images within class have variations represented as heterogeneity. Pixel-based classifiers use means/variance-covariance...
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MRI brain tumor detection using optimal possibilistic fuzzy C-means clustering algorithm and adaptive k-nearest neighbor classifier
Brain tumor characterizes the aggregation of abnormal cells in specific tissues of the brain zone. The prior distinguishing proof of brain tumors has...
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Gaussian Collaborative Fuzzy C-Means Clustering
For most FCM-based fuzzy clustering algorithms, several problems, such as noise, non-spherical clusters, and size-imbalanced clusters, are difficult...
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Analysis of Clustering Algorithms for Facility Location Allocation Problems
Problems with facility location include selecting where to site a facility in order to meet the specified constraints most effectively. Choosing a...
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An improved image clustering algorithm based on Kernel method and Tchebychev orthogonal moments
In this paper, we introduce a new clustering algorithm called Improved Kernel Possibilistic Fuzzy C-Means algorithm (ImKPFCM), based on the kernel...
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A shadowed set-based three-way clustering ensemble approach
As one of the essential topics in ensemble learning, a clustering ensemble is employed to aggregate multiple base patterns to generate a single...
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Kernel-Based Fuzzy Intuitionistic Possibilistic Clustering: Analyzing High-Dimensional Gene Expression Cancer Database
Identifying cohesion of genes for subtypes of diseases in a high-dimensional gene expression database is a highly challenging problem, since the...
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Application of Generalized Possibilistic Fuzzy C-Means Clustering for User Profiling in Mobile Networks
User profiling is the process of constructing a normal profile by accumulating the past calling behavior of a user. The technique of clustering...
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Fuzzy C-Means Hybrid with Fuzzy Bacterial Colony Optimization
In Data mining, Fuzzy or soft clustering is one of the popular approaches proposed to solve several real-world problems. The Fuzzy C-Means (FCM)...
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Enhanced Possibilistic C-Means Clustering on Big Data While Ensuring Security
Data clustering is the most important technique in knowledge discovery and data engineering. Recently, the possibilistic C-means algorithm (PCM) was...
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Satellite Image Segmentation and Classification Using Fuzzy C-Means Clustering and Support Vector Machine Classifier
Feature extraction and classification are important areas of research in image processing and computer vision with an extreme great number of...