Clustering the Concentrations of PM10 and O3: Application of Spatio-temporal Model-based Clustering

Abstract Air pollution data are large-scale dataset which can be analyzed in low scales by clustering to recognize the pattern of pollution and have simpler and more comprehensible interpretation. So, this study aims to cluster the days of year 2017 according to the hourly O3 and PM10 amounts collected from four stations of Tabriz by using spatio-temporal mixture model-based clustering (STMC). Besides, mixture model-based clustering with temporal dimension (TMC) and mixture model-based clustering without considering spatio-temporal dimensions (MC) were utilized to compare with STMC. To evaluate the efficiency of these three models and obtain the optimal number of clusters in each model, BIC and ICL criteria were used. According to BIC and ICL, STMC outperforms TMC and MC. Three clusters for O3 and four clusters for PM10 were selected as the optimal number of clusters to fit STMC models. Regarding PM10, the average concentration was the highest in cluster 4. Regarding O3, all summer days were in cluster 3 and the average concentration of this cluster was the highest. Cluster 2 had the lowest concentration with a high difference from clusters 1 and 3 and its average temperature was the lowest. Autumn days make up about 84% of this cluster. The clustering of polluted and clean days into separate groups and observing the effect of meteorological factors on the amount of concentration in each cluster clearly prove the efficiency of the model. Results of STMC showed that efficiency of clustering in air pollution data increases by considering both spatio-temporal dimensions.

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On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples

Journal of Multivariate Analysis ◽

10.1016/j.jmva.2004.02.002 ◽

2004 ◽

Vol 90 (1) ◽

pp. 90-105 ◽

Cited By ~ 25

Author(s):

G.J. McLachlan ◽

N. Khan

Keyword(s):

Mixture Model ◽

Number Of Clusters ◽

Tissue Samples ◽

Model Based Clustering ◽

Model Based

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Method for determining optimal number of clusters in K-means clustering algorithm

Journal of Computer Applications ◽

10.3724/sp.j.1087.2010.01995 ◽

2010 ◽

Vol 30 (8) ◽

pp. 1995-1998 ◽

Cited By ~ 18

Author(s):

Shi-bing ZHOU ◽

Zhen-yuan XU ◽

Xu-qing TANG

Keyword(s):

Clustering Algorithm ◽

Optimal Number ◽

Number Of Clusters ◽

Optimal Number Of Clusters

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Clustering Count-based RNA Methylation Data Using a Nonparametric Generative Model

Current Bioinformatics ◽

10.2174/1574893613666180601080008 ◽

2018 ◽

Vol 14 (1) ◽

pp. 11-23 ◽

Cited By ~ 3

Author(s):

Lin Zhang ◽

Yanling He ◽

Huaizhi Wang ◽

Hui Liu ◽

Yufei Huang ◽

...

Keyword(s):

Clustering Analysis ◽

Methylation Level ◽

Optimal Number ◽

Generative Model ◽

Methylation Data ◽

Sequencing Data ◽

Number Of Clusters ◽

Rna Methylation ◽

Clustering Effect ◽

Optimal Number Of Clusters

Background: RNA methylome has been discovered as an important layer of gene regulation and can be profiled directly with count-based measurements from high-throughput sequencing data. Although the detailed regulatory circuit of the epitranscriptome remains uncharted, clustering effect in methylation status among different RNA methylation sites can be identified from transcriptome-wide RNA methylation profiles and may reflect the epitranscriptomic regulation. Count-based RNA methylation sequencing data has unique features, such as low reads coverage, which calls for novel clustering approaches. Objective: Besides the low reads coverage, it is also necessary to keep the integer property to approach clustering analysis of count-based RNA methylation sequencing data. Method: We proposed a nonparametric generative model together with its Gibbs sampling solution for clustering analysis. The proposed approach implements a beta-binomial mixture model to capture the clustering effect in methylation level with the original count-based measurements rather than an estimated continuous methylation level. Besides, it adopts a nonparametric Dirichlet process to automatically determine an optimal number of clusters so as to avoid the common model selection problem in clustering analysis. Results: When tested on the simulated system, the method demonstrated improved clustering performance over hierarchical clustering, K-means, MClust, NMF and EMclust. It also revealed on real dataset two novel RNA N6-methyladenosine (m6A) co-methylation patterns that may be induced directly by METTL14 and WTAP, which are two known regulatory components of the RNA m6A methyltransferase complex. Conclusion: Our proposed DPBBM method not only properly handles the count-based measurements of RNA methylation data from sites of very low reads coverage, but also learns an optimal number of clusters adaptively from the data analyzed. Availability: The source code and documents of DPBBM R package are freely available through the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/DPBBM/.

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