标题： 基于向量量化谱聚类的大豆全基因组序列应用 显示英文标题

标题： Vector Quantized Spectral Clustering applied to Soybean Whole Genome Sequences

摘要： 我们开发了一种向量量化谱聚类（VQSC）算法，该算法是谱聚类（SC）和向量量化（VQ）采样的结合，用于对大豆基因组进行分组。 这里的灵感是利用SC的准确性以及VQ使算法计算成本低廉（SC的复杂度在输入规模上是三次方）。 尽管SC和VQ的结合并不新鲜，但我们工作的创新之处在于开发了SC中的关键相似性矩阵，以及在VQ中使用k-中值，两者都针对大豆基因组数据进行了调整。 我们将我们的方法与常用的UPGMA（无权重对图方法与算术平均）和NJ（邻接法）技术进行了比较。 实验结果表明，我们的方法在聚类质量（最多提高25%的聚类质量）和时间复杂度（快一个数量级）方面显著优于这两种技术。 显示英文摘要

摘要： 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).