data_e

A bimodal dataset with an inlier. one typical class is a very dense cluster,

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

data_e function

A data frame with 2001 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

A bimodal dataset with  an inlier — data_e

A data frame with 2001 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>

data_e: A bimodal dataset with an inlier

Description

Usage

Arguments

Format