计算机工程与应用2018，Vol.54Issue(9)：42-46,5.DOI:10.3778/j.issn.1002-8331.1701-0353
采用多样性选择的量子粒子群双向聚类算法
Biclustering algorithm using diversify optional quantum particle swarm optimi-zation
摘要
Abstract
One of the important tools for analyzing gene expression data is biclustering method. It focuses on finding a subset of genes and a subset of experimental conditions that together exhibit coherent behavior.However,biclustering is a multiple objective local search algorithm.When dealing with gene expression data,the results fall into local optimal area very easily.To overcome this defect and improve the global search ability of the algorithm,this paper proposes a diversity optional quantum particle swarm biclustering algorithm(Diversify-Optional QPSO,DOQPSO).Firstly,algorithm uses DOQPSO to process genetic data,and then uses the improved greedy iterative FLOC to search for biclustering,in order to achieve the more ideal results.Comparing with FLOC and QPSO,the experimental results show that DOQPSO bicluster-ing algorithm has better global convergence ability,and better clustering effect.关键词
双向聚类/基因表达数据/量子粒子群算法/多样性选择/FLOC算法Key words
biclustering/gene expression/Quantum-behaved Particle Swarm Optimization(QPSO)/diversified options/Flexible Overlapped Biclustering(FLOC)分类
信息技术与安全科学引用本文复制引用
陈佳瑜,李梁,罗云..采用多样性选择的量子粒子群双向聚类算法[J].计算机工程与应用,2018,54(9):42-46,5.基金项目
重庆市应用开发计划项目(No.CSTC2013yykf A40002). （No.CSTC2013yykf A40002）
