Density Based Anamoly Detection
- Density-based methods identify anomalies based on the local density of data points. Outliers are often located in regions with lower data density.
- The dbscan package in R is commonly used for density-based clustering, which can be adapted for anomaly detection.
R
# Install and load the dbscan package#install.packages("dbscan")library(dbscan)# Generate some example dataset.seed(123)data <- matrix(rnorm(200), ncol = 2)# Implement density-based clustering for anomaly detectionresult <- dbscan(data, eps = 0.5, minPts = 5)# Print the clustering resultprint(result)# Identify noise points (potential anomalies)anomalies <- which(result$cluster == 0)# Print the indices of potential anomaliesprint(anomalies) |
Output:
DBSCAN clustering for 100 objects.
Parameters: eps = 0.5, minPts = 5
Using euclidean distances and borderpoints = TRUE
The clustering contains 1 cluster(s) and 20 noise points.
0 1
20 80
Available fields: cluster, eps, minPts, dist, borderPoints
[1] 8 13 18 21 25 26 35 37 39 43 44 49 57 64 70 72 74 78 96 97
Anomaly Detection Using R
Anomaly detection is a critical aspect of data analysis, allowing us to identify unusual patterns, outliers, or abnormalities within datasets. It plays a pivotal role across various domains such as finance, cybersecurity, healthcare, and more.