ReviewPeerJ. Computer science2024
Comprehensive analysis of clustering algorithms: exploring limitations and innovative solutions.
Review in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- Subtypes of pre-diabetes in rural adolescent girls from India (DERVAN-11).BMJ open diabetes research & care · 2026Article
- Study on Natural Background Levels of Iron and Manganese in Groundwater of Yinchuan Plain.Water environment research : a research publication of the Water Environment Federation · 2026Article
- Identification of distinct HIV reservoir phenotypes and associated immune landscapes.JCI insight · 2026Article
- Clustering analysis of hamstring injuries-A pilot study to determine phenogroups based on injury characteristics and recovery duration.Journal of experimental orthopaedics · 2026Article
- Multi-Omics and Artificial Intelligence in Cardiovascular Medicine: From Mechanistic Insights to Clinical Translation.Biomedicines · 2026Review
- Bridging Traditional Modeling and Artificial Intelligence in Measles Epidemiology: Methods, Applications, and Future Directions-A Narrative Review.Journal of clinical medicine · 2026Review
- Metabolic trajectories in developing human neocortical neurons.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Differences in heart DNA methylation between sudden infant death syndrome and other sudden deaths.Clinical epigenetics · 2026Article
- Integrating heterogeneous data to address endemic diseases in broiler production: insights from a Polish case study.BMC veterinary research · 2026Article
- Enhancing classification accuracy in medical datasets using a hybrid distance and cluster refinement-based K-means clustering method.Scientific reports · 2026Article
- Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.Journal of nanobiotechnology · 2026Review
- H-NGPCA: Hierarchical clustering of data streams with adaptive number of clusters and adaptive dimensionality.PloS one · 2026Article
- SPICEiST: subcellular RNA pattern enhances cell clustering of imaging-based spatial transcriptomics.Genomics & informatics · 2025Article
- The Constrained Disorder Principle: A Paradigm Shift for Accurate Interactome Mapping and Information Analysis in Complex Biological Systems.Bioengineering (Basel, Switzerland) · 2025Review
- Article
- Automating the Analysis of Substrate Reactivity through Environment Interaction Mapping.Journal of chemical information and modeling · 2025Article
- AI Approaches to Homogeneous Catalysis with Transition Metal Complexes.ACS catalysis · 2025Review
- Optimizing Class Imbalance in Facial Expression Recognition Using Dynamic Intra-Class Clustering.Biomimetics (Basel, Switzerland) · 2025Article
- Nanoparticle Skin Penetration: Depths and Routes Modeled In-Silico.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Unsupervised machine learning analysis of optical coherence tomography radiomics features for predicting treatment outcomes in diabetic macular edema.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This survey rigorously explores contemporary clustering algorithms within the machine learning paradigm, focusing on five primary methodologies: centroid-based, hierarchical, density-based, distribution-based, and graph-based clustering. Through the lens of recent innovations such as deep embedded clustering and spectral clustering, we analyze the strengths, limitations, and the breadth of application domains-ranging from bioinformatics to social network analysis. Notably, the survey introduces novel contributions by integrating clustering techniques with dimensionality reduction and proposing advanced ensemble methods to enhance stability and accuracy across varied data structures. This work uniquely synthesizes the latest advancements and offers new perspectives on overcoming traditional challenges like scalability and noise sensitivity, thus providing a comprehensive roadmap for future research and practical applications in data-intensive environments.
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What OpenQuestion holds
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.