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This research introduces a new strategy in cluster ensemble selection byusing Independency and Diversity metrics. In recent years, Diversity andQuality, which are two metrics in evaluation procedure, have been used forselecting basic clustering results in the cluster ensemble selection. Althoughquality can improve the final results in cluster ensemble, it cannot controlthe procedures of generating basic results, which causes a gap in prediction ofthe generated basic results' accuracy. Instead of quality, this paperintroduces Independency as a supplementary method to be used in conjunctionwith Diversity. Therefore, this paper uses a heuristic metric, which is basedon the procedure of converting code to graph in Software Testing, in order tocalculate the Independency of two basic clustering algorithms. Moreover, a newmodeling language, which we called as "Clustering Algorithms IndependencyLanguage" (CAIL), is introduced in order to generate graphs which depictIndependency of algorithms. Also, Uniformity, which is a new similarity metric,has been introduced for evaluating the diversity of basic results. As acredential, our experimental results on varied different standard data setsshow that the proposed framework improves the accuracy of final resultsdramatically in comparison with other cluster ensemble methods.

A new selection strategy for selective cluster ensemble based on Diversity and Independency