wheel1

A bivariate dataset with an inlier and anoutlier

datasets

This is a modification of 'HDoutliers' package. The 'HDoutliers' algorithm is a powerful
unsupervised algorithm for detecting anomalies in high-dimensional data, with a
strong theoretical foundation. However, it suffers from some limitations that
significantly hinder its performance level, under certain circumstances. This package
implements the algorithm proposed in Talagala, Hyndman and Smith-Miles (2019)
<arXiv:1908.04000> for detecting anomalies in high-dimensional data
that addresses these limitations of 'HDoutliers' algorithm. We define an anomaly as an observation that deviates markedly from the majority
with a large distance gap. An approach based on extreme value theory is used
for the anomalous threshold calculation.

Priyanga Dilini Talagala

stray

Anomaly Detection in High Dimensional and Temporal Data

Rob J Hyndman

Kate Smith-Miles

wheel1 function

A data frame with 1002 rows and 3 variables:<dl class="dl-horizontal">
<dt>x </dt><dd>numerical variable</dd>
<dt>y </dt><dd>numerical variable</dd>
<dt>type</dt><dd>Type of a data point : Typical or Outlier</dd></dl>

Format

wheel data set with inlier and outlier. — wheel1

A data frame with 1002 rows and 3 variables:<dl class='dl-horizontal'>
<dt>x </dt><dd>numerical variable</dd>
<dt>y </dt><dd>numerical variable</dd>
<dt>type</dt><dd>Type of a data point : Typical or Outlier</dd></dl>

wheel1: wheel data set with inlier and outlier.

Description

Usage

Arguments

Format