{ "_id": "6ac77a47de46216d5edd08cc", "Package": "opticskxi", "Title": "OPTICS K-Xi Density-Based Clustering", "Version": "1.2.1", "Authors@R": "person(\"Thomas\", \"Charlon\", role = c(\"aut\", \"cre\"), email = \"charlon@protonmail.com\", comment = c(ORCID = \"0000-0001-7497-0470\"))", "Description": "Density-based clustering methods are well adapted to the\nclustering of high-dimensional data and enable the discovery of\ncore groups of various shapes despite large amounts of noise.\nThis package provides a novel density-based cluster extraction\nmethod, OPTICS k-Xi, and a framework to compare k-Xi models\nusing distance-based metrics to investigate datasets with\nunknown number of clusters. The vignette first introduces\ndensity-based algorithms with simulated datasets, then presents\nand evaluates the k-Xi cluster extraction method. Finally, the\nmodels comparison framework is described and experimented on 2\ngenetic datasets to identify groups and their discriminating\nfeatures. The k-Xi algorithm is a novel OPTICS cluster\nextraction method that specifies directly the number of\nclusters and does not require fine-tuning of the steepness\nparameter as the OPTICS Xi method. Combined with a framework\nthat compares models with varying parameters, the OPTICS k-Xi\nmethod can identify groups in noisy datasets with unknown\nnumber of clusters. Results on summarized genetic data of 1,200\npatients are in Charlon T. (2019)\n. 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