# How to interpret contrasting information from the Variation of Information, Dunn and Rand Index for comparing clusterings

There are related questions but the answers don't seem to explain how to practically judge these measurements for non stats users. I have a dataset which I clustered with K=4 using hierarchical clustering (complete and average methods) and kmeans, and have calculated VI, Rand, and Dunn indexes. I know this is very simplistic but from what I understand: for these measures higher is better (clusters are farther apart). But these metrics give me contrasting answers.

Methods HC-comp HC-avg K-means
IV   1.6137 1.5365 1.5225
Rand 0.2703 0.2667 0.3914
Dunn 0.08468 0.07358 0.08006


Judging from these numbers: HC-comp is better according to IV and Dunn, but according to Rand K-means seem a better choice. Should I user other cluster statistics like average silhouette width, separation, diameter,etc?