{ "_id": "6ae194f58d250e7ad09ed47d", "Package": "dbscan", "Title": "Density-Based Spatial Clustering of Applications with Noise\n(DBSCAN) and Related Algorithms", "Version": "1.2.8", "Date": "2026-xx-xx", "Authors@R": "c(\nperson(\"Michael\", \"Hahsler\", email = \"mhahsler@lyle.smu.edu\",\nrole = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0003-2716-1405\")),\nperson(\"Matthew\", \"Piekenbrock\", role = c(\"aut\", \"cph\")),\nperson(\"Sunil\", \"Arya\", role = c(\"ctb\", \"cph\")),\nperson(\"David\", \"Mount\", role = c(\"ctb\", \"cph\")),\nperson(\"Claudia\", \"Malzer\", role = \"ctb\")\n)", "Description": "A fast reimplementation of several density-based\nalgorithms of the DBSCAN family. Includes the clustering\nalgorithms DBSCAN (density-based spatial clustering of\napplications with noise) and HDBSCAN (hierarchical DBSCAN), the\nordering algorithm OPTICS (ordering points to identify the\nclustering structure), shared nearest neighbor clustering, and\nthe outlier detection algorithms LOF (local outlier factor) and\nGLOSH (global-local outlier score from hierarchies). The\nimplementations use the kd-tree data structure (from library\nANN) for faster k-nearest neighbor search. An R interface to\nfast kNN and fixed-radius NN search is also provided. Hahsler,\nPiekenbrock and Doran (2019) .", "License": "GPL (>= 3)", "URL": "https://github.com/mhahsler/dbscan,\nhttp://michael.hahsler.net/dbscan/", "BugReports": "https://github.com/mhahsler/dbscan/issues", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Copyright": "ANN library is copyright by University of Maryland, Sunil\nArya and David Mount. 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