【摘要】 为应对仍在肆虐的新冠，许多研究者对新冠基因序列的进行分析，而聚类分析就是一种有效的分析手段。论文以聚类算法为核心对新冠的基因序列数据进行分析，采用了分步降维和聚类相结合的方式，解决高维的基因序列突变数据不适合直接进行聚类的问题。通过使用两个公共数据集对几种常用的降维和聚类组合后的算法进行训练，选出最佳组合的PCA+UMAP降维及Birch聚类算法，最后将新冠基因序列的突变数据输入算法模型进行分类。同时也进一步分析确定了新冠病毒的S蛋白突变频率很高，S蛋白与该病毒的高感染性密切相关，这和相关研究者所得出的结论一致。更多还原

【Abstract】 In order to deal with the COVID-19 that is still raging,many researchers analyze the gene sequence of COVID-19,and cluster analysis is an effective means of analysis. This paper analyzes the gene sequence data of COVID-19 with clustering algorithm as the core,and uses a combination of stepwise dimension reduction and clustering to solve the problem that high-dimensional gene sequence mutation data is not suitable for direct clustering. By using two common datasets to train several commonly used algorithms after dimension reduction and clustering combination,the best combination of PCA+UMAP dimension reduction and Birch clustering algorithm is selected. Finally,the mutation data of COVID-19 gene sequence is input into the algorithm model for classification. At the same time,further analysis also confirmed that the mutation frequency of S protein of COVID-19 is very high,and S protein is closely related to the high infectivity of the virus,which is consistent with the conclusions reached by relevant researchers.更多还原