  We develop a Vector Quantized Spectral Clustering (VQSC) algorithm that is a
combination of Spectral Clustering (SC) and Vector Quantization (VQ) sampling
for grouping Soybean genomes. The inspiration here is to use SC for its
accuracy and VQ to make the algorithm computationally cheap (the complexity of
SC is cubic in-terms of the input size). Although the combination of SC and VQ
is not new, the novelty of our work is in developing the crucial similarity
matrix in SC as well as use of k-medoids in VQ, both adapted for the Soybean
genome data. We compare our approach with commonly used techniques like UPGMA
(Un-weighted Pair Graph Method with Arithmetic Mean) and NJ (Neighbour
Joining). Experimental results show that our approach outperforms both these
techniques significantly in terms of cluster quality (up to 25% better cluster
quality) and time complexity (order of magnitude faster).
