Germplasm evaluation using cluster ensemble

Abstract

Germplasm Evaluation is an integral component of Plant Genetic Resources Management. Before utilization, it is of utmost importance that the germplasm is properly characterized, evaluated and documented to have its exact identification. Since there a component of GXE interaction, it is always desired that the germplasm be evaluated to identify stable and regional specific genotypes for their proper utilization. To improve the existing methodology of germplasm evaluation, new techniques are needed to be used. Cluster ensemble enables the maximum utilization of germplasm evaluation in refined manner. For implementation of this technique, there is a need for implementation of cluster ensemble algorithm for generation of core set. The existing methods utilized for germplasm evaluation are hierarchical and non-hierarchical traditional clustering algorithms. The clustering is judged by the quality. The quality of clustering can be improved by utilizing the cluster ensemble methods. Cluster Ensemble process (technique) generates clustering which gives better results as compared to all the traditional clustering algorithm. RCDA algorithm runs on chickpea germplasm dataset and critical evaluation shows that RCDA cluster ensemble algorithm improved to a great extent as compared to traditional clustering algorithm.

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