{ "_id": "6a100160acfb0bcc41c7bbe0", "Package": "odetector", "Type": "Package", "Title": "Outlier Detection Using Partitioning Clustering Algorithms", "Version": "1.0.0", "Date": "2022-10-01", "Author": "Zeynel Cebeci [aut, cre]\n(), Cagatay Cebeci [ctb]\n(), Yalcin Tahtali [ctb]\n()", "Maintainer": "Zeynel Cebeci ", "Authors@R": "c(person(given=\"Zeynel\", family=\"Cebeci\", email = \"zcebeci@cukurova.edu.tr\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-7641-7094\")),\nperson(given=\"Cagatay\", family=\"Cebeci\", email = \"cagatay.cebeci@strath.ac.uk\", role = \"ctb\", comment = c(ORCID = \"0000-0003-2644-1261\")),\nperson(\"Yalcin\", \"Tahtali\", email = \"yalcin.tahtali@gop.edu.tr\", role = \"ctb\", comment = c(ORCID = \"0000-0003-0012-0611\")))", "Description": "An object is called \"outlier\" if it remarkably deviates\nfrom the other objects in a data set. Outlier detection is the\nprocess to find outliers by using the methods that are based on\ndistance measures, clustering and spatial methods (Ben-Gal,\n2005 ). It is one of the intensively\nstudied research topics for identification of novelties,\nfrauds, anomalies, deviations or exceptions in addition to its\nuse for outlier removing in data processing. This package\nprovides the implementations of some novel approaches to detect\nthe outliers based on typicality degrees that are obtained with\nthe soft partitioning clustering algorithms such as Fuzzy\nC-means and its variants.", "Encoding": "UTF-8", "License": "GPL (>= 2)", "URL": "https://github.com/zcebeci/odetector", "BugReports": "https://github.com/zcebeci/odetector/issues", "LazyData": "true", "VignetteBuilder": "knitr", "Repository": "https://zcebeci.r-universe.dev", "Date/Publication": "2022-10-11 13:25:29 UTC", "RemoteUrl": "https://github.com/zcebeci/odetector", "RemoteRef": "HEAD", "RemoteSha": "745aec179081e933b6506b9cfc02cb04f499338d", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-22 07:07:01 UTC", "User": "root" }, "MD5sum": "6f6bb8532ecb60fc867252dae3ec27f6", "_user": "zcebeci", "_type": "src", "_file": "odetector_1.0.0.tar.gz", "_fileid": "fc0e1d18cbc7e9005857b74afd7e36d967e6cd23909ea18cce359efc87e44ac6", "_filesize": 1190308, "_sha256": 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"anomaly detection", "cluster analysis", "unsupervised learning" ], "topics": [ "remove.outliers" ] }, { "page": "summary.outliers", "title": "Summary of outliers", "concept": [ "outlier detection", "anomaly detection", "cluster analysis", "unsupervised learning" ], "topics": [ "summary.outliers" ] }, { "page": "x3p4c", "title": "Synthetic data set consists of three variables with four clusters", "concept": [ "synthetic datasets", "clustering", "cluster analysis" ], "topics": [ "x3p4c" ] } ], "_readme": "https://github.com/zcebeci/odetector/raw/HEAD/README.md", "_rundeps": [ "inaparc", "kpeaks", "lhs", "MASS", "ppclust", "Rcpp" ], "_vignettes": [ { "source": "odetector.Rmd", "filename": "odetector.html", "title": "Outlier Detection Using Possibilistic and Fuzzy Clustering Algorithms", "author": "Zeynel Cebeci, Cagatay Cebeci & Yalcin Tahtali", "engine": "knitr::rmarkdown", "headings": [ "Preparing for the analysis", "Install and load the package odetector", "Load the data set", 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