  Clustering genotypes based upon their phenotypic characteristics is used to
obtain diverse sets of parents that are useful in their breeding programs. The
Hierarchical Clustering (HC) algorithm is the current standard in clustering of
phenotypic data. This algorithm suffers from low accuracy and high
computational complexity issues. To address the accuracy challenge, we propose
the use of Spectral Clustering (SC) algorithm. To make the algorithm
computationally cheap, we propose using sampling, specifically, Pivotal
Sampling that is probability based. Since application of samplings to
phenotypic data has not been explored much, for effective comparison, another
sampling technique called Vector Quantization (VQ) is adapted for this data as
well. VQ has recently given promising results for genome data.
  The novelty of our SC with Pivotal Sampling algorithm is in constructing the
crucial similarity matrix for the clustering algorithm and defining
probabilities for the sampling technique. Although our algorithm can be applied
to any plant genotypes, we test it on the phenotypic data obtained from about
2400 Soybean genotypes. SC with Pivotal Sampling achieves substantially more
accuracy (in terms of Silhouette Values) than all the other proposed
competitive clustering with sampling algorithms (i.e. SC with VQ, HC with
Pivotal Sampling, and HC with VQ). The complexities of our SC with Pivotal
Sampling algorithm and these three variants are almost same because of the
involved sampling. In addition to this, SC with Pivotal Sampling outperforms
the standard HC algorithm in both accuracy and computational complexity. We
experimentally show that we are up to 45% more accurate than HC in terms of
clustering accuracy. The computational complexity of our algorithm is more than
a magnitude lesser than HC.
