A Formal Algorithm for Verifying the Validity of Clustering Results Based on Model Checking

Huang, Shaobin; Cheng, Yuan; Lang, Dapeng; Chi, Ronghua; Li, Guofeng

doi:10.1371/journal.pone.0090109

Cited by 6 publications

(6 citation statements)

References 31 publications

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“…42 If the purity value is equal to 0, the quality of the clustering is poor; if the purity value is equal to 1, the quality of the clustering is excellent. 43 When the purity values are within the ranges of >0.8, ≤ 0.8, > 0.5, and ≤0.5, the corresponding methods are categorized into those with superior, good, and poor performance, respectively. Criterion Cb: Consistency of Metabolic Markers Identified in Different Subgroups.…”

Section: Comprehensive Collection Of Methods For Metabolicmentioning

confidence: 99%

MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics

Yang,

Chen,

Jiang

et al. 2024

Anal. Chem.

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Multiclass metabolomics has become a popular technique for revealing the mechanisms underlying certain physiological processes, different tumor types, or different therapeutic responses. In multiclass metabolomics, it is highly important to uncover the underlying biological information on biosamples by identifying the metabolic markers with the most associations and classifying the different sample classes. The classification problem of multiclass metabolomics is more difficult than that of the binary problem. To date, various methods exist for constructing classification models and identifying metabolic markers consisting of well-established techniques and newly emerging machine learning algorithms. However, how to construct a superior classification model using these methods remains unclear for a given multiclass metabolomic data set. Herein, MultiClassMetabo has been developed for constructing a superior classification model using metabolic markers identified in multiclass metabolomics. MultiClassMetabo can enable online services, including (a) identifying metabolic markers by marker identification methods, (b) constructing classification models by classification methods, and (c) performing a comprehensive assessment from multiple perspectives to construct a superior classification model for multiclass metabolomics. In summary, MultiClassMetabo is distinguished for its capability to construct a superior classification model using the most appropriate method through a comprehensive assessment, which makes it an important complement to other available tools in multiclass metabolomics. MultiClassMetabo can be accessed at http://idrblab.cn/ multiclassmetabo/.

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Section: Comprehensive Collection Of Methods For Metabolicmentioning

confidence: 99%

MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics

Yang,

Chen,

Jiang

et al. 2024

Anal. Chem.

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“…Therefore, the biomarker discovery method used for identifying markers was considered to be performing well. Third, a well-established measure ( purity ) was calculated based on eq and selected to assess the clustering of different classes. , p u r i t y = ∑ i = 1 K 1 N max j ( n i j ) …”

Section: Methodsmentioning

confidence: 99%

“…Third, a well-established measure (purity) was calculated based on eq 1 and selected to assess the clustering of different classes. 23,24…”

Section: Collection Of Multiple Criteria For Comprehensive Assessment...mentioning

confidence: 99%

Critical Assessment of the Biomarker Discovery and Classification Methods for Multiclass Metabolomics

2023

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Multiclass metabolomics has been widely applied in clinical practice to understand pathophysiological processes involved in disease progression and diagnostic biomarkers of various disorders. In contrast to the binary problem, the multiclass classification problem is more difficult in terms of obtaining reliable and stable results due to the increase in the complexity of determining exact class decision boundaries. In particular, methods of biomarker discovery and classification have a significant effect on the multiclass model because different methods with significantly varied theories produce conflicting results even for the same dataset. However, a systematic assessment for selecting the most appropriate methods of biomarker discovery and classification for multiclass metabolomics is still lacking. Therefore, a comprehensive assessment is essential to measure the suitability of methods in multiclass classification models from multiple perspectives. In this study, five biomarker discovery methods and nine classification methods were assessed based on four benchmark datasets of multiclass metabolomics. The performance assessment of the biomarker discovery and classification methods was performed using three evaluation criteria: assessment a (cluster analysis of sample grouping), assessment b (biomarker consistency in multiple subgroups), and assessment c (accuracy in the classification model). As a result, 13 combining strategies with superior performance were selected under multiple criteria based on these benchmark datasets. In conclusion, superior strategies that performed consistently well are suggested for the discovery of biomarkers and the construction of a classification model for multiclass metabolomics.

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“…Distance determines whether an object should be included in a specific cluster. Previous studies have proposed and used various distances, some of which are Euclidean, Manhattan, and dynamic time warping (DTW) [19][20][21].…”

Section: Similarity Distancementioning

confidence: 99%

“…where k is the number of the formed clusters, p is the number of variables, a y is the values of the a-th object, j y is the mean of the j-th object, ij y is the mean of the j-th object in the i-th cluster, j n is the number of j-th objects, and ij n is the number of j-th object in the i-th cluster [21].…”

Section: Clustering Evaluationmentioning

confidence: 99%

MTSClust with Handling Missing Data Using VAR-Moving Average Imputation

Rohaeti¹,

Sumertajaya²,

Wigena³

et al. 2023

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Modeling and forecasting multivariate time series (MTS) data with multiple objects may be challenging, especially if the data have volatility and missing data. Several studies on inflation data have been proposed, but these studies either did not use MTS data or did not consider missing data. This study aims to develop an approach that can obtain general models and forecasts for MTS data with volatility and missing data. We proposed Vector Autoregressive Moving Average Imputation Method -Multivariate Time Series Clustering (VAR-IMMA -MTSClust) to group the objects into clusters. The clusters can then be used to obtain general models and forecasts. This study consists of three stages. The first stage is the imputation simulation stage, where 10%, 20%, and 30% of MTS data were randomly removed and imputed using the original VAR-IM and the proposed VAR-IMMA. The second stage is the clustering stage where six clustering methods, i.e., K-means Euclidean, K-means Manhattan, K-means DTW, PAM Euclidean, PAM Manhattan, and PAM DTW, were used on both the completed data and the imputed data from the first stage. The third stage is the modeling and forecasting stage, where clusters from the second stage are used to obtain general models and forecasts for each cluster. The simulations were performed 1000 times and evaluated using RMSE, RMSSTD, R-squared, ARI, and balanced accuracy. The results showed that VAR-IMMA could increase the imputation accuracy by 10% in 50% of cases and even more in another 25% of cases. This increase in imputation accuracy was proven beneficial in the second stage, where clustering on imputed data formed clusters that are still like the completed data clusters despite missing data. K-means Euclidean and PAM Euclidean are two of the best methods. Finally, the use of VAR-IMMA and PAM Euclidean on inflation rate data with missing data was illustrated. The imputed clusters have an ARI score of 0.57 and balanced accuracy of 92%, leading to similar models and forecasts to the ones in the completed data.

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A Formal Algorithm for Verifying the Validity of Clustering Results Based on Model Checking

Cited by 6 publications

References 31 publications

MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics

MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics

Critical Assessment of the Biomarker Discovery and Classification Methods for Multiclass Metabolomics

MTSClust with Handling Missing Data Using VAR-Moving Average Imputation

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