• LOF

  • Referenced in 82 articles [sw19311]
  • Identifying density-based local outliers. For many KDD applications, such as detecting criminal activities ... commerce, finding the rare instances or the outliers, can be more interesting than finding ... common patterns. Existing work in outlier detection regards being an outlier as a binary property ... each object a degree of being an outlier. This degree is called the local outlier...
  • ROBPCA

  • Referenced in 54 articles [sw11592]
  • diagnostic plot that displays and classifies the outliers. We apply the algorithm to several datasets...
  • LIBRA

  • Referenced in 25 articles [sw10553]
  • robust procedures. These methods are resistant to outliers in the data. Currently, the library contains ... Squares Regression (RSIMPLS), classification (RDA, RSIMCA), clustering, outlier detection for skewed data (including the bagplot ... tools are provided for model checking and outlier detection. Most of the functions require...
  • MOA

  • Referenced in 32 articles [sw11966]
  • machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems...
  • Rainbow

  • Referenced in 27 articles [sw01263]
  • data sets for functional data display and outlier detection...
  • MixSim

  • Referenced in 26 articles [sw07914]
  • simulating Gaussian and non-Gaussian data, simulating outliers and noise variables, calculating various measures...
  • TRAMO

  • Referenced in 20 articles [sw24172]
  • Regression with ARlMA Noise, Missing Observations, and Outliers...
  • wwcode

  • Referenced in 17 articles [sw26044]
  • data deviate from normality and/or contain outliers. These procedures can be generalized by introducing weights...
  • outliers

  • Referenced in 8 articles [sw07333]
  • package outliers: Tests for outliers. A collection of some tests commonly used for identifying outliers...
  • tlmec

  • Referenced in 10 articles [sw11119]
  • errors, such assumptions make inferences vulnerable to outliers. The sensitivity to outliers and the need...
  • FSDA

  • Referenced in 14 articles [sw11737]
  • pharmaceutical problems, where the presence of outliers, multiple groups, deviations from normality and other complex...
  • MacSpin

  • Referenced in 10 articles [sw14078]
  • data as well as highly unusual observations (outliers). The program offers a broad range...
  • MVN

  • Referenced in 10 articles [sw20752]
  • tests and graphical approaches and implements multivariate outlier detection and univariate normality of marginal distributions...
  • SOREX

  • Referenced in 4 articles [sw10972]
  • SOREX: subspace outlier ranking exploration toolkit. Outlier mining is an important data analysis task ... distinguish exceptional outliers from regular objects. In recent research novel outlier ranking methods propose ... focus on outliers hidden in subspace projections of the data. However, focusing only ... detection of outliers these approaches miss to provide reasons why an object should be considered...
  • Sheppack

  • Referenced in 9 articles [sw07352]
  • when the data is known to contain outliers. SHEPPACK also includes a hybrid robust piecewise...
  • dd_tools

  • Referenced in 9 articles [sw14334]
  • toolbox provides methods for generating artificial outliers, estimating the different errors the classifiers make (false...
  • otrimle

  • Referenced in 9 articles [sw17889]
  • Clustering. Performs robust cluster analysis allowing for outliers and noise that cannot be fitted...
  • PROGRESS

  • Referenced in 9 articles [sw26316]
  • regression method is highly robust to outliers in the data. It can be computed...
  • cftool

  • Referenced in 8 articles [sw14885]
  • statistics, display confidence intervals and residuals, remove outliers and assess fits with validation data. Automatically...
  • HDDM

  • Referenced in 8 articles [sw16420]
  • full Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports...