Results 251 to 260 of about 797,470 (304)

scTACL: a multitask topology-aware contrastive learning approach for single-cell transcriptomics analysis. [PDF]

Bioinformatics
Zhou M, Lu X, Liang Y, Kow AWC, Wang G, Liu Q, Zhao Y.   +6 more
europepmc   +1 more source

Multidimensional Structural Echocardiographic Patterns and Risk Score for Prognostic Stratification in Ischemic Cardiomyopathy. [PDF]

J Clin Med
Tang R, Xu Y, Zong X, Pan R, Jia S, Xi R, Tao R, Fan Q.   +7 more
europepmc   +1 more source

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Model-Based Clustering with Nested Gaussian Clusters

Journal of Classification, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jason Hou-Liu, Ryan P. Browne
openaire   +2 more sources

Model-Based Clustering

2020
Finite mixture models are being commonly used in a wide range of applications in practice concerning density estimation and clustering. An attractive feature of this approach to clustering is that it provides a sound statistical framework in which to assess the important question of how many clusters there are in the data and their validity.
McLachlan, G. J., Rathnayake, S. I., Lee, S. X.   +2 more
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Model-Based Clustering with HDBSCAN*

2021
We propose an efficient model-based clustering approach for creating Gaussian Mixture Models from finite datasets. Models are extracted from HDBSCAN* hierarchies using the Classification Likelihood and the Expectation Maximization algorithm. Prior knowledge of the number of components of the model, corresponding to the number of clusters, is not ...
Michael Strobl, Jörg Sander 0001, Ricardo J. G. B. Campello, Osmar R. Zaïane   +3 more
openaire   +1 more source

Model-based overlapping clustering

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, 2005
While the vast majority of clustering algorithms are partitional, many real world datasets have inherently overlapping clusters. Several approaches to finding overlapping clusters have come from work on analysis of biological datasets. In this paper, we interpret an overlapping clustering model proposed by Segal et al.
Arindam Banerjee 0001, Chase Krumpelman, Joydeep Ghosh, Sugato Basu, Raymond J. Mooney   +4 more
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Model-based Clustering of Count Processes

Journal of Classification, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tin Lok James Ng, Thomas Brendan Murphy
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Model‐based linear clustering

Canadian Journal of Statistics, 2010
AbstractThe authors propose a profile likelihood approach to linear clustering which explores potential linear clusters in a data set. For each linear cluster, an errors‐in‐variables model is assumed. The optimization of the derived profile likelihood can be achieved by an EM algorithm.
Yan, Guohua, Welch, William J., Zamar, Ruben H.   +2 more
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