How to build a mean prototype from data I have a dataset with physiological measures of subjects along time. I would like to create (or select) a mean prototype example in order to be able to identify in new examples how far are they from the mean prototype. A second issue will be to select a threshold to determine what is considered near or far.
Each example has 20 numeric features and I have around 300 examples per subject.

First ideas (disregarding outliers):


To iterate through all the examples of a subject and find the one who has the minimum mean distance to all the other examples. This will select a specific example from the dataset.
To use an evolutionary algorithm to find a prototypical example which has the lowest mean distance to all the other examples. This will create a new example that can be used as prototype.


Now I would like to determine when a new example is close, far or very far from the prototype (mean). A possible approach is to set two thresholds distance to determine to which class or case corresponds the new example (close, far or very far). How could I determine these thresholds? Possibly using number of standard deviations? What other approaches can be followed to perform all this?

Let's assume the distance metric is already selected.
The object with the lowest mean distance (= the object with the lowest distance sum) is known as the medoid and the basis of k-medoids algorithms such as PAM. Because you must not use k-means with arbitrary distances.