By constructing a hashing layer as a hidden layer of the autoencoder, hash learning is performed together with unsupervised clustering by minimizing the overall loss. AUCH can unify unsupervised clustering and retrieval tasks into a single learning model. In addition, the method can use a deep ...
During the clustering process, edge nodes have a variety of trustworthy attribute characteristics. We assign different attribute weights according to the relative importance of each attribute in the clustering process, and a larger weight means that the attribute occupies a greater weight in the ...
Clustering scRNA-seq data plays a vital role in scRNA-seq data analysis and downstream analyses. Many computational methods have been proposed and achieved remarkable results. However, there are several limitations of these methods. First, they do not fully exploit cellular features. Second, they ...
Already in 2017, a dataset obtained with USRP N210 SDR has served to experimentally validate a Gaussian Mixture Model (GMM) based clustering algorithm to authenticate users in mission critical machine type communications [28]. Adaptive neural networks have been suggested as a method of achieving ...
Nilashi, M.; Dalvi-Esfahani, M.; Roudbaraki, M.Z.; Ramayah, T.; Ibrahim, O. A Multi-Criteria Collaborative Filtering Recommender System Using Clustering and Regression Techniques.J. Soft Comput. Decis. Support Syst.2016,3, 5. [Google Scholar] ...
1. Pattern recognition and clustering Pattern recognition is a mature field in computer science with well-established techniques for the assignment of unknown patterns to categories, or classes. Apatternis defined as a vector of some number of measurements, calledfeatures.Usually, a pattern recognition...
Image clustering is a complex procedure, which is significantly affected by the choice of image representation. Most of the existing image clustering methods treat representation learning and cluster...
If \({n}_{i}\, <\, 2\), then the clustering coefficient of node \(i\) is considered as zero. Global reaching centrality The global reaching centrality (GRC) of a network is calculated to evaluate the overall importance of a node based on its ability to reach other nodes in the ...
When tested on real scRNA-seq datasets, AutoImpute performed competitively wrt., the existing single-cell imputation methods, on the grounds of expression recovery from subsampled data, cell-clustering accuracy, variance stabilization and cell-type separability....
implemented a series of experiments to test the performance of AENEA, DNGR, SDNE and so on, on the standardized datasets 20-NewsGroup and Wine. The experimental results show that the performance of AENEA is obviously superior to the existing algorithms in clustering, classification and ...