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.
Keywords: FCM algorithm, Mixture of data types, Nonlinear principal component analysis, Lentil and Validity measures
Year: 2014
Volume: 74
Issue: 2
Article DOI: 10.5958/0975-6906.2014.00155.2
Print ISSN: 0019-5200
Online ISSN: 0975-6906
Girish K. Jha, Chiranjit Mazumderand Gajab Singh info_circle
Jyoti Kumari info_circle