Assessing the Trustworthiness of Clustering Solutions Obtained by a Function Optimization Scheme

Abstract

We present a method for finding clustering structures which are good and trustable. The method analyzes re-clustering results obtained by varying the search path in the space of partitions. From the scatter of results the joint optimum of given quality criteria is determined and the re-occurrence probability of this optimum (called optimum consensus) is estimated. Then the finest structure is determined that emerged robustly with scores typical of high partition quality. When applied to tumor gene expression benchmark data the method assigned fewer tissue samples to a wrong class compared to methods based on either consensus or quality criteria.