Results 81 to 90 of about 797,470 (304)
Graphical Tools for Model-based Mixture Discriminant Analysis [PDF]
, 2012 Visualization and graphics can play an important role in understanding discriminant analysis. Fisher's canonical variates provide a graphical counterpart to linear discriminant analysis (LDA).
SCRUCCA, Luca
core
Spherical k-Means Clustering [PDF]
, 2012 Clustering text documents is a fundamental task in modern data analysis, requiring approaches which perform well both in terms of solution quality and computational efficiency.
Kurt Hornik +7 more
core +2 more sources
Scientific Reports, 2022 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 ...
Joon-Tae Kim +8 more
doaj +1 more source
Regularization and optimization in model-based clustering
Pattern RecognitionDue to their conceptual simplicity, k-means algorithm variants have been extensively used for unsupervised cluster analysis. However, one main shortcoming of these algorithms is that they essentially fit a mixture of identical spherical Gaussians to data that vastly deviates from such a distribution. In comparison, general Gaussian Mixture Models (GMMs)
Raphael Araujo Sampaio +3 more
openaire +2 more sources
FEBS Letters, EarlyView.An unexpected alternative interaction site for ethyl viologen was identified in formate dehydrogenase 1 from Methylorubrum extorquens. Combined mutagenesis, kinetic analysis, and docking revealed that aromatic residues near an iron–sulfur cluster enable flavin mononucleotide‐independent electron transfer, offering a framework for engineering improved ...
Eleni G. Poloniataki, Yong Hwan Kim
wiley +1 more source
Model-based clustering of non-Gaussian panel data based on skew-t distributions [PDF]
, 2010 We propose a model-based method to cluster units within a panel. The underlying model is autoregressive and non-Gaussian, allowing for both skewness and fat tails, and the units are clustered according to their dynamic behavior.
Juárez, Miguel A. +2 more
core +1 more source
Recommendation algorithm based on GMM clustering and FOA-GRNN model
网络与信息安全学报, 2018 Aiming at the problem of low recommendation accuracy caused by sparse data in traditional item-based recommendation algorithm,a recommendation algorithm based on GMM clustering and FOA-GRNN model is proposed.The algorithm firstly uses Gaussian mixture ...
Yipeng LI, Yeli RUAN, Jie ZHANG
doaj +3 more sources
Model-based longitudinal clustering with varying cluster assignments
Statistica Sinica, 2017 It is often of interest to perform clustering on longitudinal data, yet it is difficult to formulate an intuitive model for which estimation is computationally feasible. We propose a model-based clustering method for clustering objects that are observed over time.
Sewell, Daniel K. +3 more
openaire +2 more sources
FEBS Letters, EarlyView.Proteostasis and the gut microbiota play a key role in shaping host physiology. Microbiota‐derived metabolites, vitamins, and RNA modulate host proteostasis. Findings from model systems, including C. elegans, indicate microbes can either stabilize or disrupt host proteostasis.
Abhishek Anil Dubey, Maria Ermolaeva
wiley +1 more source
High-dimensional Clustering with Random Projections [PDF]
, 2018 Random projections (RPs) have shown to provide promising results for high-dimensional classification. In this work, we address the issue of high-dimensional clustering by exploiting the general idea of RP ensemble to perform unsupervised classification ...
Francesca Fortunato +2 more
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