A new approach for evaluating internal cluster validation indices
Zoltán Botta-Dukát
A vast number of different methods are available for unsupervised
classification. Since no algorithm and parameter setting performs best in all
types of data, there is a need for cluster validation to select the actually
best-performing algorithm. Several indices were proposed for this purpose
without using any additional (external) information. These internal validation
indices can be evaluated by applying them to classifications of datasets with a
known cluster structure. Evaluation approaches differ in how they use the
information on the ground-truth classification. This paper reviews these
approaches, considering their advantages and disadvantages, and then suggests a
new approach.