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Cluster-based ensemble learning for wind power modeling with meteorological w...
Optimal implementation and monitoring of wind energy generation hinge on reliable power modeling that is vital for understanding turbine control, farm operational optimization,... -
Clustering by transitive propagation
The dataset used in the paper is a set of data points with likelihood functions f0 and f1 describing the probability that two data points are from the same cluster or different... -
Federated cINN Clustering Algorithm
Federated cINN Clustering Algorithm (FCCA) uses MNIST, FMNIST, Cifar10, Cifar100 and Synthetic datasets for experiments. -
Medical Expenditure
The dataset contains medical information of various patients collected for research purposes. The race attribute is considered for fairness. -
Frequency Response Data of 30 VCM Plants
Frequency response data of 30 VCM plants, clustered using k-medoids and Gaussian Mixture Models -
Homoglyphs and Clustering in Unicode
The dataset used in the paper is a collection of Unicode characters, with a focus on identifying homoglyphs and clustering them into equivalence classes. -
Correlation Clustering
Graph neural networks (GNNs) are a powerful family of models that operate over graph-structured data and have achieved state-of-the-art performance on node and graph... -
Multi-Decoder RNN Autoencoder Based on Variational Bayes Method
Time series data analysis, clustering algorithms, recurrent neural network, variational Bayes method -
Clustering categorical data via ensembling
We present a technique for clustering categorical data by generating many dissimilarity matrices and averaging over them. -
Weighted Point Sets for Weight-Balanced k-Means
The dataset is used to test the weight-balanced k-means algorithm for weighted point sets and prescribed lower and upper bounds on the cluster sizes. -
Clustering Dataset
The dataset used in the paper is a collection of examples for the task of clustering. -
Exponential Family Mixture Model
The dataset used in this paper is a finite mixture model with a finite number of k components from a parametric statistical model P. -
Finite Mixture Model
The dataset used in this paper is a finite mixture model with a finite number of k components from a parametric statistical model P. -
iCmSC: Incomplete Cross-Modal Subspace Clustering
Incomplete Cross-Modal Subspace Clustering -
Generative Partial Multi-view Clustering With Adaptive Fusion and Cycle Consi...
Generative Partial Multi-view Clustering With Adaptive Fusion and Cycle Consistency -
CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network
Cognitive Deep Incomplete Multi-view Clustering Network -
Partial Multi-View Clustering
Partial multi-view clustering -
Multi-View Clustering via Deep Matrix Factorization
Multi-view clustering via deep matrix factorization -
Incomplete Multi-view Clustering via Cross-view Relation Transfer
Incomplete multi-view clustering on incomplete views -
Bregman Power k-Means
Exponential family data
