基于蚁群优化聚类算法的DNA序列分类方法

针对目前聚类算法在分析DNA序列数据时的低效性和分类精度低问题，提出一种基于蚁群优化聚类算法（ACOC）的DNA序列分类方法，在密度函数中加入自适应感应量并应用模拟退火中的α-适应量的冷却策略，采用DNA序列分布特征对DNA序列进行特征提取，并将pearson相关系数引入蚁群聚类算法作为相似性度量。在EMBL-DNA数据库中4个数据集上进行性能测试，与统计聚类和k-means算法的比较表明，该方法具有一定的时间和精度的优越性，适于解决大规模DNA序列数据分类问题。

DNA sequence classification based on ant colony optimization clustering algorithm

A modification of ant-based clustering algorithm for DNA sequence analysis is presented.For increasing the efficiency of ant-based clustering algorithm in terms of running time and accuracy, the modified version of ACOC has incorporated two main modifications in relation to ACA：An adaptive perception scheme occurs in the density function and a cooling scheme of a-adaptation.The features of DNA sequence are extracted according to Di-nucleotide frequency.Then pearson correlation coefficient is used to analyze the relationship.Experimental results on EMBL-DNA datasets clearly show that ACOC performs well when this paper is compared to statistics clustering and k-means and is suitable for Mass DNA sequence classification.