DB: Distance-based outlier detection based on user-given...

In DDoutlier: Distance & Density-Based Outlier Detection

Description

Function to calculate how many observations are within a certain sized neighborhood as an outlier score. Outliers are classified according to a user-given threshold of observations to be within the neighborhood. Suggested by Knorr, M., & Ng, R. T. (1997)

Usage

DB(dataset, d = 1, fraction = 0.05)

Arguments

dataset	The dataset for which observations are classified as outliers/inliers
d	The radius of the neighborhood
fraction	The proportion of the number of observations to be within the neighborhood for observations to be classified as inliers. If the proportion of observations within the neighborhood is less than the given fraction, observations are classified as outliers

Details

DB computes a neighborhood for each observation given a radius (argument 'd') and returns the number of neighbors within the neighborhood. Observations are classified as inliers or outliers, based on a proportion (argument 'fraction') of observations to be within the neighborhood

Value

neighbors	The number of neighbors within the neighborhood
classification	Binary classification of observations as inlier or outlier

Author(s)

Jacob H. Madsen

References

Knorr, M., & Ng, R. T. (1997). A Unified Approach for Mining Outliers. In Conf. of the Centre for Advanced Studies on Collaborative Research (CASCON). Toronto, Canada. pp. 236-248. DOI: 10.1145/782010.782021

Examples

# Create dataset
X <- iris[,1:4]

# Classify observations
cls_observations <- DB(dataset=X, d=1, fraction=0.05)$classification

# Remove outliers from dataset
X <- X[cls_observations=='Inlier',]

Example output

DDoutlier documentation built on May 1, 2019, 10:20 p.m.