Comprehensive assessment of triclustering algorithms for three-way temporal data analysis-Reference-Cited by-同舟云学术

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Comprehensive assessment of triclustering algorithms for three-way temporal data analysis

Published:2024-06 Issue: Volume:150 Page:110303
ISSN:0031-3203
Container-title:Pattern Recognition
language:en
Short-container-title:Pattern Recognition

Author:

Soares Diogo F.^ORCID,Henriques Rui,C. Madeira Sara

Funder

Fundação para a Ciência e a Tecnologia

Publisher

Elsevier BV

Reference38 articles.

1. Triclustering algorithms for three-dimensional data analysis: A comprehensive survey;Henriques;ACM Comput. Surv.,2018

2. Triclustering-based classification of longitudinal data for prognostic prediction: targeting relevant clinical endpoints in amyotrophic lateral sclerosis;Soares;Sci. Rep.,2023

3. TRICLUSTER: An effective algorithm for mining coherent clusters in 3D microarray data;Zhao,2005

4. gTRICLUSTER: a more general and effective 3d clustering algorithm for gene-sample-time microarray data;Jiang,2006

5. Efficiently mining time-delayed gene expression patterns;Wang;IEEE Trans. Syst. Man Cybern. B,2009

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Temporal stratification of amyotrophic lateral sclerosis patients using disease progression patterns;Nature Communications;2024-07-08

2. Biclustering data analysis: a comprehensive survey;Briefings in Bioinformatics;2024-05-23

3. Refined allocation of power marketing service resources based on clustering analysis algorithm;Proceedings of the 2023 4th International Conference on Big Data Economy and Information Management;2023-12-08

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