ArticlePloS one2019
Clustering algorithms: A comparative approach.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers, 1 of them a synthesis that pooled it.
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Who cites it
111 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Subtyping irritable bowel syndrome using cluster analysis: a systematic review.BMC bioinformatics · 2023Pooled it
- AI-Based Phenotyping of Atrial Fibrillation Through Generative Topographic Mapping: Prospective Murcia Atrial Fibrillation Project III Cohort Study.Journal of medical Internet research · 2026Observational
- Perioperative Metabolomic Profiling in Neonatal Cardiac Surgery Identifies a High-Risk Phenotype for Adverse Outcomes.JACC. Advances · 2026Article
- Clustering-cum-regression based model and performance analysis for early prediction of heart disease.Scientific reports · 2026Article
- A fast integrative clustering and feature selection approach for high-dimensional multiview data.Statistical methods in medical research · 2026Article
- Factor Analysis-Based Quantitative Endotyping Improves Associations With CRS Cross-Sectional and Longitudinal Outcomes.International forum of allergy & rhinology · 2026Article
- Addressing Survivorship Bias in Neurocognitive Outcomes After Early Complex Cardiac Surgery Using Clustering and Propensity Scores.CJC pediatric and congenital heart disease · 2025Article
- Evolution of the Metazoan Protein Domain Repertoire Revealed by a Birth-Death-Gain Model.Journal of molecular evolution · 2025Article
- FLACON: An Information-Theoretic Approach to Flag-Aware Contextual Clustering for Large-Scale Document Organization.Entropy (Basel, Switzerland) · 2025Article
- Assessing the Role of Composition and Size Effects in the Hydrogen Evolution Reaction on NiACS omega · 2025Article
- A Comprehensive Review of Remote Sensing and Artificial Intelligence Integration: Advances, Applications, and Challenges.Sensors (Basel, Switzerland) · 2025Review
- Improved WOA-DBSCAN Online Clustering Algorithm for Radar Signal Data Streams.Sensors (Basel, Switzerland) · 2025Article
- Clustering of electronic health records in atrial fibrillation patients and impact on prognosis and patient trajectories: a UK linked-dataset study.European heart journal. Digital health · 2025Article
- Impact of distance measures in adaptive K-means clustering on load profiles and spatial patterns of distributed substations in Thailand.Scientific reports · 2025Article
- DONKEY: A Flexible and Accurate Algorithm for Clustering.Journal of chemical theory and computation · 2025Article
- How quantum computing can enhance biomarker discovery.Patterns (New York, N.Y.) · 2025Review
- Relation equivariant graph neural networks to explore the mosaic-like tissue architecture of kidney diseases on spatially resolved transcriptomics.Bioinformatics (Oxford, England) · 2025Article
- Knowledge and practice of healthy behaviors for dementia and stroke prevention in a United States cohort.Scientific reports · 2025Article
- A comprehensive and quantitative SEM-EDS analytical process applied to lithium-ion battery electrodes.Scientific reports · 2025Article
- Spatiotemporal effect of internet use on life expectancy: cross-country insight from a geographically and temporally weighted analysis.BMC public health · 2025Article
51 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many real-world systems can be studied in terms of pattern recognition tasks, so that proper use (and understanding) of machine learning methods in practical applications becomes essential. While many classification methods have been proposed, there is no consensus on which methods are more suitable for a given dataset. As a consequence, it is important to comprehensively compare methods in many possible scenarios. In this context, we performed a systematic comparison of 9 well-known clustering methods available in the R language assuming normally distributed data. In order to account for the many possible variations of data, we considered artificial datasets with several tunable properties (number of classes, separation between classes, etc). In addition, we also evaluated the sensitivity of the clustering methods with regard to their parameters configuration. The results revealed that, when considering the default configurations of the adopted methods, the spectral approach tended to present particularly good performance. We also found that the default configuration of the adopted implementations was not always accurate. In these cases, a simple approach based on random selection of parameters values proved to be a good alternative to improve the performance. All in all, the reported approach provides subsidies guiding the choice of clustering algorithms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.