A Study on the Relationship between Internal and External Validity Indices Applied to Partitioning and Density-based Clustering Algorithms

Tomasini, Caroline; Borges, Eduardo Nunes; Machado, Karina; Emmendorfer, Leonardo Ramos

doi:10.5220/0006317000890098

Cited by 8 publications

(4 citation statements)

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“…In relation to clustering performance, it achieved a Dunn index equal to 0.79 and Gamma Index of 0.81. For these indexes, the closer to 1 the better the grouping ( Hassani and Seidl, 2017 ;Tomasini et al, 2017 ). At follow-up, five distinct clusters were found: "minimal" (34%), "mild" (26%), "moderate" (13%), "negative and depressive" (13%) and "severe symptoms" (14%) ( Fig.…”

Section: Clinical Subgroups Of Patients At Baseline and Follow-upmentioning

confidence: 95%

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Identifying clinical clusters with distinct trajectories in first-episode psychosis through an unsupervised machine learning technique

Amoretti

Verdolini

Mezquida

et al. 2021

European Neuropsychopharmacology

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Section: Clinical Subgroups Of Patients At Baseline and Follow-upmentioning

confidence: 95%

“…The Silhouette indicates how well objects are clustered, ranging from −1 (poorly) to 1 (well clustered). The Dunn index provides the ratio between compactness within a cluster and separation between different clusters ( Hassani and Seidl, 2017 ;Tomasini et al, 2017 ).…”

Section: Clinical Subgroups Of Patients At Baseline and Follow-upmentioning

confidence: 99%

Identifying clinical clusters with distinct trajectories in first-episode psychosis through an unsupervised machine learning technique

Amoretti

Verdolini

Mezquida

et al. 2021

European Neuropsychopharmacology

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“…The optimal number of clusters can be determined using the Silhouette coefficient method [Tomasini. et al 2017].…”

Section: Clustering Evaluation Based On Silhouette Coefficientmentioning

confidence: 99%

A clustering algorithm to evaluate the attitude of Brazilian researchers regarding open access research data

Freitas¹,

Bottero²,

Lucca³

et al. 2021

ENIAC

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The core point of the research process are data. They are records from scientific investigation, which support the results published in journals and conferences. Making research data available in open access digital repositories has many advantages, such as increasing the visibility of associated publications, reproducing experiments, and validating results. In Brazil, full and unrestricted sharing of them is not yet accepted by most researchers. This paper presents an initial study to describe a model analyzing the attitude of Brazilian researchers concerning open access research data. A clustering algorithm was used to identify different research profiles. The achieved results indicate the main reasons why the researchers object to share their data.

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“…First, we used hierarchical cluster analysis (HCA) to investigate the presence of cognitive subgroups in the BD sample using Ward's method and squared Euclidean distance using R package 'cluster' (version 2.1.0) (Hermens et al, 2011(Hermens et al, , 2015. We computed the average silhouette width to determine optimal numbers of clusters with package 'factoextra' (version 1.0.5) (Tomasini, Borges, Machado, & Emmendorfer, 2017). We performed a Discriminant function analysis (DFA) using package 'MASS' (version 7.3-51.4) to confirm the clusters retained and to investigate the predictive power of the clustering of each participant's cognitive domain to the cognitive subgroup (Jensen et al, 2016).…”

Section: Cognitive Subgroupsmentioning

confidence: 99%

Data-driven cognitive phenotypes in subjects with bipolar disorder and their clinical markers of severity

et al. 2020

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Background Subjects with bipolar disorder (BD) show heterogeneous cognitive profile and that not necessarily the disease will lead to unfavorable clinical outcomes. We aimed to identify clinical markers of severity among cognitive clusters in individuals with BD through data-driven methods. Methods We recruited 167 outpatients with BD and 100 unaffected volunteers from Brazil and Spain that underwent a neuropsychological assessment. Cognitive functions assessed were inhibitory control, processing speed, cognitive flexibility, verbal fluency, working memory, short- and long-term verbal memory. We performed hierarchical cluster analysis and discriminant function analysis to determine and confirm cognitive clusters, respectively. Then, we used classification and regression tree (CART) algorithm to determine clinical and sociodemographic variables of the previously defined cognitive clusters. Results We identified three neuropsychological subgroups in individuals with BD: intact (35.3%), selectively impaired (34.7%), and severely impaired individuals (29.9%). The most important predictors of cognitive subgroups were years of education, the number of hospitalizations, and age, respectively. The model with CART algorithm showed sensitivity 45.8%, specificity 78.4%, balanced accuracy 62.1%, and the area under the ROC curve was 0.61. Of 10 attributes included in the model, only three variables were able to separate cognitive clusters in BD individuals: years of education, number of hospitalizations, and age. Conclusion These results corroborate with recent findings of neuropsychological heterogeneity in BD, and suggest an overlapping between premorbid and morbid aspects that influence distinct cognitive courses of the disease.

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A Study on the Relationship between Internal and External Validity Indices Applied to Partitioning and Density-based Clustering Algorithms

Cited by 8 publications

References 0 publications

Identifying clinical clusters with distinct trajectories in first-episode psychosis through an unsupervised machine learning technique

Identifying clinical clusters with distinct trajectories in first-episode psychosis through an unsupervised machine learning technique

A clustering algorithm to evaluate the attitude of Brazilian researchers regarding open access research data

Data-driven cognitive phenotypes in subjects with bipolar disorder and their clinical markers of severity

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