Nonlinear principal component based fuzzy clustering: A case study of lentil genotypes

Abstract

Cluster analysis is frequently used by the plant breeders in grouping germplasm collections into a few homogeneous groups in order to identify accessions with specific property of potential relevance for their plant improvement programs. The set of descriptors for the germplasm accessions consists of both numerical and categorical descriptors. In such situations, the standard principal component analysis will not be appropriate for feature extraction of data using all descriptors because it deals with only numeric variables. In this paper, nonlinear principal component analysis was used to analyse the descriptors of lentil accessions which can handle mixture of measurement types. The first two nonlinear principal components were used as input to fuzzy c-means algorithm in grouping 518 lentil genotypes into four clusters based on their agronomic and morphological traits. The study demonstrated that the proposed nonlinear principal component based fuzzy clustering has a promising potential in agriculture as a tool for evaluation and efficient grouping of germplasm collections.