Abstract
Phenotypic characteristics of a plant species refers to its physical properties as cataloged by plant biologists at different research centers around the world. Clustering species 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 generated promising results for genotypic 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 species, we tested it on the phenotypic data obtained from about 2,400 Soybean species. 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 the 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 less than that of HC.
Highlights
Genetic diversity has been an important foundation of plant breeding from the inception of agriculture since it helps develop new plants to meet the growing food demand globally
The hypothesis related to this is as follows: for a particular sampling technique, if the estimate of the population total using the samples is close to the actual population total, that sampling technique is considered good in an absolute sense
We demonstrate that use of sampling with modified Spectral Clustering (SC) does not deteriorate the quality of clustering
Summary
Genetic diversity has been an important foundation of plant breeding from the inception of agriculture since it helps develop new plants to meet the growing food demand globally. The breeding process is a complex combination of multiple stages [1]. The first stage involves discovery of the native characteristics where the selection of diverse parent donors is of paramount importance [2]. One way plant genetic diversity can be studied is by using their phenotypic characteristics (physical characteristics). This kind of analysis can be relatively done because a sufficiently large amount of data is available from different geographical areas. In the phenotypic context, which is our first focus, a few characteristics that play an important role are Days to 50% Flowering, Days to Maturity, Plant Height, 100 Seed Weight, Seed Yield Per Plant, Number of Branches Per Plant, etc
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