首页 | 官方网站   微博 | 高级检索  
     

基于蚁群优化聚类算法的DNA序列分类方法
引用本文:梁冰,陈德运.基于蚁群优化聚类算法的DNA序列分类方法[J].计算机工程与应用,2010,46(25):124-126.
作者姓名:梁冰  陈德运
作者单位:哈尔滨理工大学 计算机科学与技术学院,哈尔滨 150080
摘    要: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。

关 键 词:DNA序列分析  蚁群聚类算法  分类  特征提取  person相关系数  
收稿时间:2009-6-12
修稿时间:2010-2-22  

DNA sequence classification based on ant colony optimization clustering algorithm
LIANG Bing,CHEN De-yun.DNA sequence classification based on ant colony optimization clustering algorithm[J].Computer Engineering and Applications,2010,46(25):124-126.
Authors:LIANG Bing  CHEN De-yun
Affiliation:College of Computer Science and Technology,Harbin University of Science and Technology,Harbin 150080,China
Abstract: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.
Keywords:DNA sequence analysis  ant-based clustering algorithm  classification  feature extraction  pearson correlation coefficient
本文献已被 维普 万方数据 等数据库收录!
点击此处可从《计算机工程与应用》浏览原始摘要信息
点击此处可从《计算机工程与应用》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司    京ICP备09084417号-23

京公网安备 11010802026262号