Compare and contrast the different techniques for anomaly detection that were presented. In particular, try to identify circumstances in which the definitions of anomalies used in the different techniques might be equivalent or situations in which one might make sense, but another would not. Be sure to consider different types of data.

Consider the following definition of an anomaly: An anomaly is an object that is unusually influential in the creation of a data model.

Compare this definition to that of the standard model$-$based definition of an anomaly.

For what sizes of data sets $($small$,$ medium, or large$)$ is this definition appropriate?