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Table 7 Clustering goals and decision rules for driving & load profiles of electric vehicles

From: A practical approach to cluster validation in the energy sector

Goal Explanation Mathematical formulation Simos Rank Weight in %
The number of clusters should be as low as possible. Since the resulting clusters are the basis for a subsequent optimization with high computation time, a lower number is favored. max(Iparsimony) 6 33.0
Clusters should be relatively even in size. The resulting representative driving & load profiles will be distributed according to their cluster size. If single clusters are overrepresented due to their size, the same driving & load profiles will be used and hence the desired variance will be low. max(Ientropy) 6 33.0
Members of a cluster should be well represented by a specific datapoint within the dataset. This is necessary in order to a) simulate driving & load profiles and b) have it be as similar to other points in the cluster as possible. Input features are a lower dimensional representation of driving & load profiles. max(Icp2cent) 5 27.7
Clusters should be describable by a low number of features. Next to having unique and distinguishable characteristics, in order to create understandable “personas”, the number of characterizing features should be as low as possible. max(Ipps) 1 6.3