Question

Which of the following options is a measure of internal evaluation of a clustering algorithm?
A) Rand index
B) Davies-Bouldin index
C) Jaccard index
D) F-measure

Answer 1

For evaluating the performance of a clustering algorithm I would suggest to use cluster validity indices. In literature several different scalar validity measures have been proposed which result to be more or less appropriate depending on your data and the specific application (Non-hierarchical, crisp, fuzzy clustering), there are several:  Root-mean-square standard deviation (RMSSTD) of the new cluster, Semi-partial R-squared (SPR),  R-squared (RS) Distance between two clusters (CD), Partition Coefficient (PC), Classification Entropy (CE), Partition Index (PC), Separation Index (S), Xie and Beni's Index (XB), Inter-Cluster Density (ID), Davies-Bouldin (DB) index, Dunn's Index (DI), Alternative Dunn Index (ADI) and so on.


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Answer 2

Which of the following options is a measure of internal evaluation of a clustering algorithm?

A) Rand index

B) Davies-Bouldin index

C) Jaccard index

D) F-measure

Answer:   B) Davies-Bouldin index