  This research introduces a new strategy in cluster ensemble selection by
using Independency and Diversity metrics. In recent years, Diversity and
Quality, which are two metrics in evaluation procedure, have been used for
selecting basic clustering results in the cluster ensemble selection. Although
quality can improve the final results in cluster ensemble, it cannot control
the procedures of generating basic results, which causes a gap in prediction of
the generated basic results' accuracy. Instead of quality, this paper
introduces Independency as a supplementary method to be used in conjunction
with Diversity. Therefore, this paper uses a heuristic metric, which is based
on the procedure of converting code to graph in Software Testing, in order to
calculate the Independency of two basic clustering algorithms. Moreover, a new
modeling language, which we called as "Clustering Algorithms Independency
Language" (CAIL), is introduced in order to generate graphs which depict
Independency of algorithms. Also, Uniformity, which is a new similarity metric,
has been introduced for evaluating the diversity of basic results. As a
credential, our experimental results on varied different standard data sets
show that the proposed framework improves the accuracy of final results
dramatically in comparison with other cluster ensemble methods.
