IBM SPSS Modeler Cookbook

Using a single cluster K-means as an alternative to anomaly detection

Cleaning data includes detecting and eliminating outliers. When outliers are viewed as a property of individual variables, it is easy to examine a data set, one variable at a time, and identify which records fall outside the usual range for a given variable. However, from a multivariate point of view, the concept of an outlier is less obvious; individual values may fall within accepted bounds but a combination of values may still be unusual.

The concept of multivariate outliers is used a great deal in anomaly detection, and this can be used both for data cleaning and more directly for applications such as fraud detection. Clustering techniques are often used for this purpose; ...