Solving cluster ensemble problems by correlation's matrix & ga

Abstract

Clustering ensembles have emerged as a powerfui method for improving both the robustness and the stability of unsupervised classification solutions. However, finding a consensus clustering from multiple partitions is a difficult problem that can be approached from graph-based, combinatorial or statistical perspectives. We offer a probabilistic model of consensus using a finite mixture of multinomial distribufions in a space of clustering, A combined partition is found as a solution to the corresponding maximum iikelihood problem using the GA algorithm. The excellent scalability of this algorithm and comprehensible underlying model are particularly important for clustering of large datasets. This study includes two sections, at the first, calculate correlation matrix.this matrix show correlation between samples and we found the best samples that can be in the center of clusters. In the other section a genetic algorithm is employed to produce the most stable partitions from an evolving ensemble (population) of clustering aigoritiims along with a special objective fonction. The objective fianction evaluates multiple partitions according to changes caused by data perturbations and prefers those clustering that are least susceptible to those perturbations.