This site allows you to try a number of different outlier or anomaly detection algorithms.

To use this page, choose your model, sample, and number of clusters. Outliers are marked with a star and cluster centers with an X.

When changing samples it is necessary to press the Update Sample Button. You may add or remove points by using the mouse button. It is also possible to highlight a region and remove all the points. This app is running on a server with very little memory, so be gentle. Some of the fuzzy clustering algorithms may take a while.

For more information on how to use this app, the models, the samples, and how to find outliers and anomalies, take a look at the tutorial section.

Author:

• Rajiv Shah

Other Projects:

• Projects

The app allows you to see the trade-offs on various types of outlier / anomaly detection algorithms. Outliers are marked with a star and cluster centers with an X.

Some things to try:

• What is the effect of scaling the data?
• How do outliers affect clustering and the identification of outliers?
• Try making two clusters with different densities, how does that affect detection?
• Try using the eyes or corner sample and place a point betweent the clusters. Which algorithms identify the point as an outlier?
• Try playing around with the smiley face or doughnut and understand how different algorithms identify points as outliers

Models used with corresponding R package in parenthesis:

• Hierarchical Clustering (DMwR)
• Kmeans (distance metrics from proxy)
  • Kmeans Euclidean Distance
  • Kmeans Mahalanobis
  • Kmeans Manhattan
• Fuzzy kmeans (all from fclust)
  • Fuzzy kmeans - Gustafson and Kessel
  • Fuzzy k-medoids
  • Fuzzy k-means with polynomial fuzzifier
• Local Outlier Factor (dbscan)
• RandomForest (proximity from randomForest)
  • Isolation Forest (IsolationForest)
• Autoencoder (Autoencoder)

Scaling function uses scale from base R

Inspiration:

• CovNetJS
• Visualizing K-Means Clustering