ArticlePloS one2024
The least sample size essential for detecting changes in clustering solutions of streaming datasets.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
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Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it, 6 citations in OpenAlex.
- Molecular endotypes in musculoskeletal disorders: a systematic review.BMC musculoskeletal disorders · 2026Pooled it
- Unsupervised Clustering in Neurocritical Care: A Systematic Review.Neurocritical care · 2025Pooled it
- Conditioned Pain Modulation and Pleasant Pain Relief as Complementary Processes Reflecting Individual Differences in Pain Coping: A Clustering Approach.European journal of pain (London, England) · 2026Article
- Clustering analysis of hamstring injuries-A pilot study to determine phenogroups based on injury characteristics and recovery duration.Journal of experimental orthopaedics · 2026Article
- Comparison of K-Means and Hierarchical Clustering Methods for Buffalo Milk Production Data.Animals : an open access journal from MDPI · 2025Article
- Uncovering the impact of outliers on clusters' evolution in temporal data-sets: an empirical analysis.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The clustering analysis approach treats multivariate data tuples as objects and groups them into clusters based on their similarities or dissimilarities within the dataset. However, in modern world, a significant volume of data is continuously generated from diverse sources over time. In these dynamic scenarios, the data is not static but continually evolves. Consequently, the interesting patterns and inherent subgroups within the datasets also change and develop over time. The researchers have paid special attention to monitoring changes in cluster solutions of evolving streams. For this matter, several algorithms have been proposed in the literature. However, to date, no study has examined the effect of variability in cluster sizes on the evolution of cluster solutions. Moreover, no guidance is available on determining the impact of cluster sizes on the type of changes they experience in the streams. In the present simulation study using artificial datasets, the evolution of clusters is examined concerning the variability in cluster sizes. The findings are substantial because tracing and monitoring the changes in clustering solutions have a wide range of applications in every field of research. This study determines the minimum sample size required in the clustering of time-stamped datasets.
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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.