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clustering algorithm dbscan  (MathWorks Inc)


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    MathWorks Inc clustering algorithm dbscan
    Clustering Algorithm Dbscan, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/clustering+algorithm+dbscan/pm38684893-299-9-6
    Average 90 stars, based on 1 article reviews
    clustering algorithm dbscan - by Bioz Stars, 2026-09
    90/100 stars

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    Article Title: Continuum architecture dynamics of vesicle tethering in exocytosis
    Article Snippet: The centroids of those supervoxels were then clustered by DBSCAN in its Matlab implementation.

    Article Title: nanoFeatures: a cross-platform application to characterize nanoparticles from super-resolution microscopy images.
    Article Snippet: MATLAB’s DBSCAN is used to identify clusters in the sections, an example of how this algorithm works is also shown.

    Article Title: Replay without sharp wave ripples in a spatial memory task
    Article Snippet: Then, the sample points were clustered using dbscan in Matlab, with a neighborhood search radius of 2.5 bins and a minimum number of neighbors of 50.

    Article Title: Simultaneous Identification of Vitamins B1, B3, B5, and B6 by an Engineered Nanopore.
    Article Snippet: DBSCAN is an unsupervised machine-learning cluster analysis algorithm, which was performed by MATLAB.

    Article Title: Computational Search for Inhibitors of SOD1 Mutant Infectivity as Potential Therapeutics for ALS Disease.
    Article Snippet: Finally, poses were clustered using DBSCAN in MATLAB based on an RMSD matrix computed in Python.

    Article Title: Replay without sharp wave ripples in a spatial memory task
    Article Snippet: Then, the sample points were clustered using dbscan in Matlab, with a neighborhood search radius of 3 bins and a minimum number of neighbors of 250.

    Article Title: Unsupervised restoration of a complex learned behavior after large-scale neuronal perturbation.
    Article Snippet: Syllable identity was defined using the Matlab clustering algorithm, dbscan.

    Article Title: Behavioural response of prey to repeated attacks by non-coordinating predators
    Article Snippet: The fraction of agents constituting the largest prey group in the domain following the predators’ attack has been computed as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$l_{c}$$\end{document} (normalised against the initial prey aggregate numbers, i.e., \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N=506$$\end{document} ) using a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm in MATLAB by considering the neighbourhood search radius as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r_{alg}$$\end{document} and the minimum number of agents within a neighbourhood to be identified as a cluster set at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1\%$$\end{document} of initial prey numbers N . With an increase in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Omega _P$$\end{document} , the prey group exhibits a transition from a cohesive escape response to a split-and-escape response.



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