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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 proficient video recommendation framework using hybrid fuzzy C means clustering and Kullback-Leibler divergence algorithms
A video recommendation framework for e-commerce clients is proposed using the collaborative filtering (CF) process. One of the most important...
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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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Classification with Incomplete Probabilistic Labeling Based on Manifold Regularization and Fuzzy Clustering Ensemble
AbstractThe paper proposes a weakly supervised binary classification method which combines manifold regularization and fuzzy clustering ensemble...
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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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Fuzzy C-Means Clustering: Advances and Challenges (Part II)
Undoubtedly, Fuzzy C-means (FCM) is considered as one of the most successful clustering algorithms since last two decades. It has been extensively...
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Efficient data routing for agricultural landscapes: ensemble fuzzy crossover based golden jackal approach
Precision agriculture involves extensive agricultural landscapes with varying terrains and crop types. An energy-efficient routing protocol ensures...
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Standard precipitation-temperature index (SPTI) drought identification by fuzzy c-means methodology
Global warming and climate change impacts intensify hydrological cycle and consequently unprecedented drought and flood appear in different parts of...
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Adaptive type2-possibilistic C-means clustering and its application to microarray datasets
Microarray technology is an important innovation that simultaneously facilitates measuring the expression level for thousands of genes in different...
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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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Advanced ensemble machine-learning and explainable ai with hybridized clustering for solar irradiation prediction in Bangladesh
The solar revolution in Bangladesh stands as a symbol of hope and self-reliance, illuminating communities and steering the nation towards a more...
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Enhancing diversity and robustness of clustering ensemble via reliability weighted measure
To solve the problem of hidden pattern recognition and high dimensional perception of geospatial sensor data, machine learning can build a model of...
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Clustering ensemble extraction: a knowledge reuse framework
Clustering ensemble combines several fundamental clusterings with a consensus function to produce the final clustering without gaining access to data...
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Local search genetic algorithm-based possibilistic weighted fuzzy c-means for clustering mixed numerical and categorical data
Clustering for mixed numerical and categorical attributes has attracted many researchers due to its necessity in many real-world applications. One...
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An ensemble algorithm integrating consensus-clustering with feature weighting based ranking and probabilistic fuzzy logic-multilayer perceptron classifier for diagnosis and staging of breast cancer using heterogeneous datasets
Breast cancer is a major threat, predominantly affecting the female population. Staging of cancer enables early detection and prognosis of patients,...
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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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DFECTS: A Deep Fuzzy Ensemble Clusterer for Time Series
Time series clustering plays an important role in various fields such as anomaly detection and resource scheduling. With the increase of complexity...
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Dual-level clustering ensemble algorithm with three consensus strategies
Clustering ensemble (CE), renowned for its robust and potent consensus capability, has garnered significant attention from scholars in recent years...
