ArticlePeerJ. Computer science2024
AutoSCAN: automatic detection of DBSCAN parameters and efficient clustering of data in overlapping density regions.
Article 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 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Optics-free spatial genomics for mapping mammalian brain aging by IRISeq.Nature neuroscience · 2026Article
- Modern RNA Quantification Methods: From RT-qPCR to Advanced Microscopy.The journal of physical chemistry. B · 2026Review
- Unsupervised clustering of serum lipase activity in cats: a data-driven approach to correlate clinical, laboratory, and ultrasonographic findings.Journal of veterinary internal medicine · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
The density-based clustering method is considered a robust approach in unsupervised clustering technique due to its ability to identify outliers, form clusters of irregular shapes and automatically determine the number of clusters. These unique properties helped its pioneering algorithm, the Density-based Spatial Clustering on Applications with Noise (DBSCAN), become applicable in datasets where various number of clusters of different shapes and sizes could be detected without much interference from the user. However, the original algorithm exhibits limitations, especially towards its sensitivity on its user input parameters minPts and
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Registered trials
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