The clusters formed with the neural network-based index were more in agreement with those defined by functional categories and common regulatory motifs.doi:10.1016/S0010-4825(02)00032-XTomohiro SawaLucila Ohno-MachadoElsevier LtdComputers in Biology & Medicine...
Neural network model with clustering ensemble approach 机译:聚类集成方法的神经网络模型 摘要 A predictive global model for modeling a system includes a plurality of local models, each having: an input layer for mapping into an input space, a hidden layer and an output layer. The hidden layer st...
Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets). machine-learningdata-miningdeep-learningclusteringsurveysrepresentation-learningdata-mining-algorithmsnetwork-embeddinggraph-convolutional-networksgcngraph-embeddinggraph-neural-networksself-su...
where the fitness function is the composed density between and within clusters (CDbw) validity index of strongly connected groups of neurons, while scanning through different values of the minimum cluster size so as to find stable regions with a reasonable trade-off between their length and their...
The quality assessment of the each cluster is done through the Partition Coefficient and Exponential Separation index. The extensive carried out experiments stress on the benefits of the introduced approach and show that it outperforms the pioneering approaches of the literature. 展开 关键词: ...
Clustering stroke patients with similar characteristics to predict subsequent vascular outcome events is critical. This study aimed to compare several clustering methods, particularly a deep neural network-based model, and identify the best clustering method with a maximally distinct 1-year outcome in pat...
1, and then searches for suitable subspaces within this new feature space. This review paper focuses on the sparsity-based clustering methods, considering sparse and redundant representations as a both feature transformation and a “structure” of clustering with the help of adaptive (learned) ...
The autoencoder is a neural network that is able to learn nonlinear representations efficiently49. There are various types of autoencoder models. The denoising autoencoder receives corrupted data with artificial noises and reconstructs the original data50. It is widely used for noisy datasets to lea...
However, the chaotic neural network does not stay in the global solution due to the chaotic dynamical mechanism being not clear. A chaotic mechanism with annealing strategy is introduced into the Hopfield network to construct a ACNN for expecting a better opportunity of converging to the optimal ...
Taking advantage of spatial transcriptomics and graph neural networks, we introduce cell clustering for spatial transcriptomics data with graph neural networks, an unsupervised cell clustering method based on graph convolutional networks to improve ab initio cell clustering and discovery of cell subtypes ...