While traditional methods of identifying outliers usually look at one or two variables at a time, anomaly detection can examine large numbers of fields to identify clusters or peer groups into which similar records fall. Each record can then be compared to others in its peer group to identify possible anomalies. The further away a case is from the normal center, the more likely it is to be unusual.
Each record is assigned an anomaly index, which is the ratio of the group deviation index to its average over the cluster that the case belongs to. The larger the value of this index, the more deviation the case has than the average. Under the usual circumstance, cases with anomaly index values less than 1 or even 1.5 would not be considered as anomalies, because the deviation is just about the same or a bit more than the average. However, cases with an index value greater than 2 could be good anomaly candidates because the deviation is at least twice the average.
Anomaly detection is an exploratory method designed for quick detection of unusual cases or records that should be candidates for further analysis. These should be regarded as suspected anomalies, which, on closer examination, may or may not turn out to be real. You may find that a record is perfectly valid but choose to screen it from the data for purposes of model building. Alternatively, if the algorithm repeatedly turns up false anomalies, this may point to an error or artifact in the data collection process.
Note that anomaly detection identifies unusual records or cases through cluster analysis based on the set of fields selected in the model without regard for any specific target (dependent) field and regardless of whether those fields are relevant to the pattern you are trying to predict. For this reason, you may want to use anomaly detection in combination with feature selection or another technique for screening and ranking fields. For example, you can use feature selection to identify the most important fields relative to a specific target and then use anomaly detection to locate the records that are the most unusual with respect to those fields.
The Anomaly node bases its findings on the results of a Two-Step Cluster analysis, which is a clustering method that can handle large numbers of features and cases, including both categorical and scale features, and allows you to specify the number of groups or clusters in the data or to have the algorithm select the number based on the data.
Like your visualization? Why not deploy it? For more information, see Deploy a model.