detect_density

Uses the Local Outlier Factor (LOF) algorithm to identify anomalies based on local density.
It is useful for detecting outliers in multi-dimensional data that Z-score misses.

Implements a suite of tools for outlier detection and treatment
in data mining. It includes univariate methods (Z-score, Interquartile Range),
multivariate detection using Mahalanobis distance, and density-based
detection (Local Outlier Factor) via the 'dbscan' package. It also
provides functions for visualization using 'ggplot2' and data cleaning
via Winsorization.

Daniel López Pérez

quickOutlier

Detect and Treat Outliers in Data Mining

detect_density function

<dl><dt>data</dt>
<dd>A data frame (only numeric columns will be used).</dd>
<dt>k</dt>
<dd>Integer. The number of neighbors to consider. Defaults to 5.</dd>
<dt>threshold</dt>
<dd>Numeric. The LOF score cutoff. Values &gt; 1 indicate potential outliers. Defaults to 1.5.</dd></dl>

Arguments

Detect Density-Based Anomalies (LOF) — detect_density

<dl>

<dt>data</dt>
<dd>A data frame (only numeric columns will be used).</dd>


<dt>k</dt>
<dd>Integer. The number of neighbors to consider. Defaults to 5.</dd>


<dt>threshold</dt>
<dd>Numeric. The LOF score cutoff. Values &gt; 1 indicate potential outliers. Defaults to 1.5.</dd>

</dl>

detect_density: Detect Density-Based Anomalies (LOF)

Description

Usage

Value

Arguments

Examples