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

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 biclustering algorithm has better global convergence ability, and better clustering effect.

Key words: biclustering, gene expression, Quantum-behaved Particle Swarm Optimization（QPSO）, diversified options, Flexible Overlapped Biclustering（FLOC）