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A robust multi-view knowledge transfer-based rough fuzzy C-means clustering algorithm
Rough fuzzy clustering algorithms have received extensive attention due to the excellent ability to handle overlap** and uncertainty of data....
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A fuzzy clustering ensemble selection based on active full-link similarity
In fuzzy clustering ensemble, the quality of fuzzy base clustering has an important influence on the performance of the final clustering result. Due...
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A Bi-directional Fuzzy C-Means Clustering Ensemble Algorithm Considering Local Information
The classic Fuzzy C-means (FCM) algorithm has limited clustering performance and is prone to misclassification of border points. This study offers a...
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Fuzzy C-Means Clustering Validity Function Based on Multiple Clustering Performance Evaluation Components
Clustering is the process of grou** a set of physical or abstract objects into multiple similar objects. Fuzzy C-means (FCM) clustering is one of...
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Efficient Big Data Clustering Using Adhoc Fuzzy C Means and Auto-Encoder CNN
Clustering, a well-known unsupervised machine learning technique is effective in handling massive amount of data for a variety of applications....
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Fuzzy Kernel Weighted Random Projection Ensemble Clustering For High Dimensional Data
A clustering ensemble seeks to treat a consensus function by taking multiple base clustering. There are generally two main limitations: (1)...
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Novel fuzzy clustering-based undersampling framework for class imbalance problem
The class imbalance problem occurs in various real-world datasets. Although it is considered that samples of the classes of a dataset are evenly...
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A Multiple Fuzzy C-Means Ensemble Cluster Forest for Big Data
Over the recent decades, there has been an exponential growth of data streaming from various data sources, such as social networks and data centers....
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Modified fuzzy clustering algorithm based on non-negative matrix factorization locally constrained
The fuzzy C-means (FCM) algorithm is a classical clustering algorithm which is widely used. However, especially for high-dimensional data sets with...
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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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Fetal Head Segmentation Using Optimized K-Means and Fuzzy C-Means on 2D Ultrasound Images
This paper presents a method for segmenting the fetal head in 2D ultrasound images using optimized K-Means and Fuzzy C-Means algorithms using...
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Skin Cancer Detection from Dermatoscopic Images Using Hybrid Fuzzy Ensemble Learning Model
Malignant tissue in the skin is highly harmful. As melanoma is of identical look and lacks color variation, detection of skin cancer from...
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An Adaptive Fuzzy C Means with Seagull Optimization Algorithm for Analysis of WSNs in Agricultural Field with IoT
In recent years, the environmental monitoring in agriculture field is an essential required application. To achieve the environmental monitoring of...
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Preserving the Legacy of Ancient Tamil Script with Deep Learning and Fuzzy C Means Algorithm: Intelligent Approach to Digiitization
Handwritten character recognition has become a focus of research since the emergence of deep learning technologies. This research objective is to...
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A novel lung extraction approach for LDCT images using discrete wavelet transform with adaptive thresholding and Fuzzy C-means clustering enhanced by genetic algorithm
PurposeLung cancer is the second most common type of cancer prevalent in men worldwide. The early diagnosis of lung cancer can reduce cancer-related...
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Machine Learning Based Power Efficient Optimized Communication Ensemble Model with Intelligent Fog Computing for WSNs
Wireless sensor networks have evolved in recent years. Energy consumption is one of the major parameters to access the performance of the network....
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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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A Novel Ensemble Methodology to Validate Fuzzy Clusters of Big Data
The clustering of datasets is a widely used technique in unsupervised machine learning. The cluster quality evaluation is a tricky problem because...
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Fuzzy C-Means Based Feature Selection Mechanism for Wireless Intrusion Detection
The wireless network devices grow rapidly and the security of these devices is quite crucial. Attackers employ new techniques and methods to deceive...
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Fuzzy particle swarm optimization (FPSO) based feature selection and hybrid kernel distance based possibilistic fuzzy local information C-means (HKD-PFLICM) clustering for churn prediction in telecom industry
AbstractCustomer churn has been considered as one of the key issues in the operations of the corporate business sector, as it influences the turnover...
