Constrained parsimonious model-based clustering

García-Escudero, Luis Ángel; Mayo-Íscar, Agustín; Riani, Marco

doi:10.1007/s11222-021-10061-3

Cited by 13 publications

(3 citation statements)

References 22 publications

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“…In our application study, for example, we have observed a large similarity between SPRM and enetLTS, which overshadows the ranking results of RSK-means. As K-means can be seen as a special case of tclust 14 , a more flexible robust clustering approach which is particularly designed to fit clusters with different scatters and weights, it would for instance be interesting to replace trimmed k-means by this more general approach blueor even more advanced versions 44 , 45 in future applications.…”

Section: Discussionmentioning

confidence: 99%

ROSIE: RObust Sparse ensemble for outlIEr detection and gene selection in cancer omics data

Jensch

Lopes

Vinga

et al. 2022

Stat Methods Med Res

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The extraction of novel information from omics data is a challenging task, in particular, since the number of features (e.g. genes) often far exceeds the number of samples. In such a setting, conventional parameter estimation leads to ill-posed optimization problems, and regularization may be required. In addition, outliers can largely impact classification accuracy. Here we introduce ROSIE, an ensemble classification approach, which combines three sparse and robust classification methods for outlier detection and feature selection and further performs a bootstrap-based validity check. Outliers of ROSIE are determined by the rank product test using outlier rankings of all three methods, and important features are selected as features commonly selected by all methods. We apply ROSIE to RNA-Seq data from The Cancer Genome Atlas (TCGA) to classify observations into Triple-Negative Breast Cancer (TNBC) and non-TNBC tissue samples. The pre-processed dataset consists of [Formula: see text] genes and more than [Formula: see text] samples. We demonstrate that ROSIE selects important features and outliers in a robust way. Identified outliers are concordant with the distribution of the commonly selected genes by the three methods, and results are in line with other independent studies. Furthermore, we discuss the association of some of the selected genes with the TNBC subtype in other investigations. In summary, ROSIE constitutes a robust and sparse procedure to identify outliers and important genes through binary classification. Our approach is ad hoc applicable to other datasets, fulfilling the overall goal of simultaneously identifying outliers and candidate disease biomarkers to the targeted in therapy research and personalized medicine frameworks.

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Section: Discussionmentioning

confidence: 99%

ROSIE: RObust Sparse ensemble for outlIEr detection and gene selection in cancer omics data

Jensch

Lopes

Vinga

et al. 2022

Stat Methods Med Res

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show abstract

“…Gaussian distribution is the normal distribution, and model-based clustering is an iterative method to fit a set of datasets into clusters [29]. This method works in three steps: First, randomly choose Gaussian parameters and fit them to a set of data points.…”

Section: Fig 4 Dbscan Cluster Visualizationmentioning

confidence: 99%

Cluster Ensemble Method and Convolution Neural Network Model for Predicting Mental Illness

Ananthapadmanabha¹,

C²,

Sabariraju³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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One in every four individuals has a diagnosable mental disease in a year. Around 20% of children and adolescents have a mental health condition and often ignore it. It is found that 93% of youth use social media to communicate and engage, as it reflects their emotions, moods, and thoughts. As a result, machine learning algorithms may anticipate people's moods and emotions based on their postings and comments. On the other hand, psychometric tests use questions to determine how individuals think, feel, behave, and react. It is necessary to investigate a hybrid approach for identifying people's mental illness by combining social media inputs and psychometric tests, especially during a pandemic. The hybrid approach can combine the results from both the models to reflect on the user's digital & non-digital reactions to certain sensitive situations to determine their mental state. Hence, the present paper aims to develop a web framework that can forecast the emergence of mental illness in the future based on data from social media comments and real-time data from psychometric tests using machine learning algorithms. The proposed work includes the cluster ensemble method for social media posts and a convolution neural network model for psychometric tests. This model predicts mental illness with an accuracy of 87.05 percent. The individual can use this result to take the required precautions by visiting a psychologist.

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“…A model-based algorithm may locate clusters by constructing a density function that reflects the spatial distribution of data points, and it also automatically decides on the number of clusters based on standard statistics, considering noisy data or isolated points, resulting in robust clustering methods [39]. Typical model-based clustering methods include statistical methods (e.g., COBWEB, CLASSIT, and AutoClass) or neural network methods (e.g., competitive learning and self-organizing feature maps) [40,41]. Model-based clustering algorithms are computationally complex and slightly inadequate for handling large-scale data sets.…”

Section: Current Status Of Research Applications Of Data Processing A...mentioning

confidence: 99%

An Improved Optimization Algorithm Based on Density Grid for Green Storage Monitoring System

et al. 2022

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This study takes a sample of green storage monitoring data for corn from a biochemical energy enterprise, based on the enterprise’s original storage monitoring system while establishing a “green fortress” intending to achieve green and sustainable grain storage. This paper proposes a set of processing algorithms for real-time flow data from the storage system based on cluster analysis to detect abnormal storage conditions, achieve the goal of green grain storage and maximize benefits for the enterprises. Firstly, data from the corn storage monitoring system and the current status of research on data processing algorithms are analyzed. Our study summarizes the processing of re-al-time stream data together with the characteristics of the monitoring system and discusses the application of clustering analysis algorithms. The study includes an in-depth study of the green storage monitoring system data for corn and the processing requirements for real-time stream data. As the main novelty of this research, the optimization algorithm model is applied to the green storage monitoring system for maize and is validated. Finally, the processing results for the green storage monitoring data for maize are presented in graphical and textual formats.

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Constrained parsimonious model-based clustering

Cited by 13 publications

References 22 publications

ROSIE: RObust Sparse ensemble for outlIEr detection and gene selection in cancer omics data

ROSIE: RObust Sparse ensemble for outlIEr detection and gene selection in cancer omics data

Cluster Ensemble Method and Convolution Neural Network Model for Predicting Mental Illness

An Improved Optimization Algorithm Based on Density Grid for Green Storage Monitoring System

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