Hello and welcome to RapidMiner

**Short anwer:**Mixed Euclidean Distance. Why ? It is the metric commonly used.

**Long answer:**Selecting a metric means you define whether two Examples are similar(*) or not. Which metric is "right" has the quality of philosophical discussion. The metric has a lot of influence on the following learning operations, so choosing the right one is crucial. This picture will illustrate the similarity problem:

*You know what similar is, when you see it !* But how define mathematically...?

Okay, seriously:

All the metrics available have different properties and choosing the right one depends on the data the metric is for. So ... given the current state of information, we are not able to make a wise suggestion and listing all properties of all metrics...I do not think I can/will do this

. But: The Mixed Euclidean Distance works for the general case...

greetings

Steffen

*although similarity "not equals" metric in the literature. I use this term here to ease the explanation.