LAUSR

Cluster ensemble techniques aim to combine the outputs of multiple clustering algorithms to obtain a single consensus partitioning. The current paper reports about the development of a cluster ensemble based technique combining the concepts of multiobjective optimization and deep-learning models for gene clustering where some additional protein-protein interaction information are utilized for generating the consensus partitioning. The proposed ensemble based framework works in four phases: (i) filtering out the irrelevant genes from the microarray dataset: only the statistically significant genes are considered for further data analysis; (ii) generation of diverse base partitionings: a multi-objective optimization-based clustering technique is proposed which simultaneously optimizes three different cluster quality measures and generates a set of partitioning solutions on the Pareto optimal front; (iii) generation of a consensus partitioning: mentha scores, calculated by accessing a highly enriched protein-protein interaction archive named mentha, of different clustering solutions are considered for generating a weighted incidence matrix; (iv) finally, two approaches are used to generate a consensus partitioning from the obtained incidence matrix. The first approach is based on a traditional machine learning method, and another approach exploits the graph partitioning algorithm and two deep neural models to generate the final clustering. To validate the efficacy of the proposed ensemble framework, it is applied on five gene expression datasets. We present a comparative analysis of the proposed technique over different clustering algorithms in terms of biological homogeneity index (BHI) and biological stability index (BSI). The traditional approach attains an average 3 and 2 percent improvements over the best non-dominated solution with respect to BHI and BSI, respectively, whereas deep learning models illustrate an average 6.8 and 1.5 percent improvements over the proposed traditional approach with respect to BHI and BSI, respectively. Subsequently, Welch's t-test is executed to prove that the results obtained by the proposed methods are statistically significant. Availability of data and materials: https://github.com/sduttap16/DeepEnsm.