A model-based clustering for time-series with irregular interval

2005 ◽  
Author(s):  
Xiao-tao Zhang ◽  
Wei Zhang ◽  
Xiong Xiong ◽  
Qi-wen Wang ◽  
Cui-yu Li
Keyword(s):  
Time Series ◽  
Model Based Clustering ◽  
Model Based ◽  
Irregular Interval

scholarly journals Maximum Pseudolikelihood Estimation for Model-Based Clustering of Time Series Data

2017 ◽  
Vol 29 (4) ◽  
pp. 990-1020 ◽  
Author(s):  
Hien D. Nguyen ◽  
Geoffrey J. McLachlan ◽  
Pierre Orban ◽  
Pierre Bellec ◽  
Andrew L. Janke
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Expectation Maximization Algorithm ◽  
Resting State Fmri ◽  
Series Data ◽  
Ml Estimation ◽  
Model Based Clustering ◽  
Model Based ◽  
Ml Estimator ◽  
Maximum Pseudolikelihood

Mixture of autoregressions (MoAR) models provide a model-based approach to the clustering of time series data. The maximum likelihood (ML) estimation of MoAR models requires evaluating products of large numbers of densities of normal random variables. In practical scenarios, these products converge to zero as the length of the time series increases, and thus the ML estimation of MoAR models becomes infeasible without the use of numerical tricks. We propose a maximum pseudolikelihood (MPL) estimation approach as an alternative to the use of numerical tricks. The MPL estimator is proved to be consistent and can be computed with an EM (expectation-maximization) algorithm. Simulations are used to assess the performance of the MPL estimator against that of the ML estimator in cases where the latter was able to be calculated. An application to the clustering of time series data arising from a resting state fMRI experiment is presented as a demonstration of the methodology.

Model-Based Clustering of Multiple Time Series

2008 ◽  
Vol 26 (1) ◽  
pp. 78-89 ◽  
Author(s):  
Sylvia Fröhwirth-Schnatter ◽  
Sylvia Kaufmann
Keyword(s):  
Time Series ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Model Based Clustering ◽  
Model Based

Novel Study of Model-Based Clustering Time Series Gene Expression in Different Tissues: Applications to Aging Process

2020 ◽  
Vol 13 (2) ◽  
pp. 178-187
Author(s):  
Farzane Ahmadi ◽  
Ali-Reza Abadi ◽  
Zahra Bazi ◽  
Abolfazl Movafagh
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Nuclear Antigen ◽  
General Mechanism ◽  
Normal Distributions ◽  
Model Based Clustering ◽  
Model Based ◽  
Therapeutic Decisions ◽  
Time Series Gene Expression ◽  
Different Tissues

Background: Aging is an organized biological process that is regulated by highly interconnected pathways between different cells and tissues in the living organism. Identification of similar genes between tissues in different ages may also help to discover the general mechanism of aging or to discover more effective therapeutic decisions. Objective: Objective: According to the wide application of model-based clustering techniques, the aim is to evaluate the performance of the Mixture of Multivariate Normal Distributions (MMNDs) as a valid method for clustering time series gene expression data with the Mixture of Matrix-Variate Normal Distributions (MMVNDs). Methods: In this study, the expression of aging data from NCBI’s Gene Expression Omnibus was elaborated to utilize proper data. A set of common genes which were differentially expressed between different tissues were selected and then clustered together through two methods. Finally, the biological significance of clusters was evaluated, using their ability to find genes in the cell using Enricher. Results: The MMVNDs is more efficient to find co-express genes. Six clusters of genes were observed using the MMVNDs. According to the functional analysis, most genes in clusters 1-6 are related to the B-cell receptors and IgG immunoglobulin complex, proliferating cell nuclear antigen complex, the metabolic pathways of iron, fat, and body mass control, the defense against bacteria, the cancer development incidence, and the chronic kidney failure, respectively. Conclusion: Results showed that most biological changes of aging between tissues are related to the specific components of immune cells. Also, the application of MMVNDs can increase the ability to find similar genes.

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