Clustering Ensemble
2개 벤치마크 · 논문 26편 · 이 태스크의 논문 보기 →
Benchmarks
ionosphere
pathbased
Most implemented
k-HyperEdge Medoids for Clustering Ensemble
Clustering Ensemble Meets Low-rank Tensor Approximation
An Internal Validity Index Based on Density-Involved Distance
The Impact of Random Models on Clustering Similarity
Papers
CAKE: Confidence in Assignments via K-partition Ensembles
Clustering is widely used for unsupervised structure discovery, yet it offers limited insight into how reliable each individual assignment is. Diagnostics, such as convergence behavior or objective values, may reflect gl…
Clustering Ensemblek-HyperEdge Medoids for Clustering Ensemble
Clustering ensemble has been a popular research topic in data science due to its ability to improve the robustness of the single clustering method. Many clustering ensemble methods have been proposed, most of which can b…
ClusteringClustering EnsembleSnapshot Spectral Clustering -- a costless approach to deep clustering ensembles generation
Despite tremendous advancements in Artificial Intelligence, learning from large sets of data in an unsupervised manner remains a significant challenge. Classical clustering algorithms often fail to discover complex depen…
ClusteringClustering EnsembleDeep ClusteringEnsemble LearningDeep Clustering With Consensus Representations
The field of deep clustering combines deep learning and clustering to learn representations that improve both the learned representation and the performance of the considered clustering method. Most existing deep cluster…
ClusteringClustering EnsembleDeep ClusteringSelective clustering ensemble based on kappa and F-score
Clustering ensemble has an impressive performance in improving the accuracy and robustness of partition results and has received much attention in recent years. Selective clustering ensemble (SCE) can further improve the…
ClusteringClustering EnsembleDiversityCEU-Net: Ensemble Semantic Segmentation of Hyperspectral Images Using Clustering
Most semantic segmentation approaches of Hyperspectral images (HSIs) use and require preprocessing steps in the form of patching to accurately classify diversified land cover in remotely sensed images. These approaches u…
ClusteringClustering EnsembleSegmentationSemantic Segmentation