The Multiple-Criteria Evidence Accumulation Clustering (Multi-EAC) method, is a clustering ensemble approach with an integrated cluster stability criteria used to selectively learn the similarity from a collection of different clustering algorithms. In this work we analyze the original Multi-EAC criteria in the context of the classical relative validation criteria, and propose alternative cluster validation indices for the selection of clusters based on pairwise similarities. Taking several clustering ensemble construction strategies as context, we compare the adequacy of each criteria and provide guidelines for its application. Experimental results on benchmark data sets show the proposed concepts.