Vector Quantized Spectral Clustering applied to Soybean Whole Genome Sequences

TL;DR

This paper introduces a novel Vector Quantized Spectral Clustering method tailored for soybean genome data, achieving higher accuracy and faster computation compared to traditional clustering techniques.

Contribution

It combines spectral clustering with vector quantization, using a new similarity matrix and k-medoids, optimized specifically for soybean genome sequences.

Findings

Outperforms UPGMA and NJ in cluster quality by up to 25%

Significantly reduces computational time

Demonstrates effectiveness on soybean genome data

Abstract

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).