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Table 6 New indices for municipality clustering

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

Name Abbreviation Usage
The number of clusters should be between 5 and 30. max(Itargetrange) Similarly to parsimony, the target range index assesses the number of resulting clusters k. If k is within this target range, the index is 1, if it is lower than the lower limit kmin, it increases linearly from 0 at k=0 to 1 at kmin. For values larger than the upper limit kmax, the value decreases analogously, reaching zero at k=kmin+kmax.
Clusters should be describable by a low number of features. max(Ipps) This parameter builds on the predictive power score (PPS) (Sharma 2020). The PPS uses machine learning to find (pairwise) linear and non-linear relations between two feature vectors. The proposed index calculates the PPS between every feature vector and the clustering results. A threshold to imply a “good” correlation between features and results is set. The mean number of features describing the resulting cluster result well is used to derive a cvi according to the Parsimony (IP) with Kmax as the dimensionality of the features.