• LOF

  • Referenced in 88 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 67 articles [sw11592]
  • diagnostic plot that displays and classifies the outliers. We apply the algorithm to several datasets...
  • Orca

  • Referenced in 32 articles [sw32638]
  • Orca: A Program for Mining Distance-Based Outliers. Orca is a program for mining outliers ... large multivariate data sets. An outlier is an example that is substantially different from ... reminder of the data. An outlier may have values for an attribute that are unusually ... rarely seen together. Orca mines distance-based outliers. That is, Orca uses the distance from...
  • mvBACON

  • Referenced in 26 articles [sw36501]
  • BACON: blocked adaptive computationally efficient outlier nominators. Although it is customary to assume that data ... homogeneous, in fact, they often contain outliers or subgroups. Methods for identifying multiple outliers ... sophisticated methods, the computation cost often makes outlier detection unattractive. All multiple outlier detection methods ... this paper (algorithms for the detection of outliers in multivariate and regression data). The algorithms...
  • LIBRA

  • Referenced in 28 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 40 articles [sw11966]
  • machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems...
  • MixSim

  • Referenced in 33 articles [sw07914]
  • simulating Gaussian and non-Gaussian data, simulating outliers and noise variables, calculating various measures...
  • Rainbow

  • Referenced in 32 articles [sw01263]
  • data sets for functional data display and outlier detection...
  • TRAMO

  • Referenced in 24 articles [sw24172]
  • Regression with ARlMA Noise, Missing Observations, and Outliers...
  • FSDA

  • Referenced in 18 articles [sw11737]
  • pharmaceutical problems, where the presence of outliers, multiple groups, deviations from normality and other complex...
  • wwcode

  • Referenced in 18 articles [sw26044]
  • data deviate from normality and/or contain outliers. These procedures can be generalized by introducing weights...
  • tlmec

  • Referenced in 11 articles [sw11119]
  • errors, such assumptions make inferences vulnerable to outliers. The sensitivity to outliers and the need...
  • MVN

  • Referenced in 15 articles [sw20752]
  • tests and graphical approaches and implements multivariate outlier detection and univariate normality of marginal distributions...
  • GET

  • Referenced in 15 articles [sw35164]
  • regions of functional or multivariate data (e.g. outlier detection, functional boxplot) and for global confidence...
  • outliers

  • Referenced in 7 articles [sw07333]
  • package outliers: Tests for outliers. A collection of some tests commonly used for identifying outliers...
  • Sheppack

  • Referenced in 12 articles [sw07352]
  • when the data is known to contain outliers. SHEPPACK also includes a hybrid robust piecewise...
  • robCompositions

  • Referenced in 12 articles [sw11804]
  • methods, methods to impute rounded zeros, (robust) outlier detection for compositional data, (robust) principal component...
  • ELKI

  • Referenced in 12 articles [sw30860]
  • unsupervised methods in cluster analysis and outlier detection. In order to achieve high performance...
  • SLOM

  • Referenced in 6 articles [sw36331]
  • SLOM: a new measure for local spatial outliers. We propose a measure, spatial local outlier ... able to discern local spatial outliers that are usually missed by global techniques, like “three ... data point and suppresses the reporting of outliers in highly unstable areas, where data ... heterogeneous and the notion of outliers is not meaningful. We prove several properties of SLOM...
  • SOREX

  • Referenced in 5 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...