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中文摘要：
为提高癌症基因表达数据聚类的准确性和效率,对具有完全学习策略的量子行为粒子群优化（CLQPSO）算法和广义回归神经网络（GRNN）进行了研究,实现了一种CLQPSO癌症基因聚类算法.GRNN能充分利用多条相似基因隐含的规律,对基因表达缺失值的预测有较高的可信度;CLQPSO算法在迭代更新时能充分利用各粒子当前最佳位置和粒子群所提供的社会合作信息,避免过早收敛于局部最优解.实验结果表明,综合使用GRNN和CLQPSO算法对癌症基因表达数据进行聚类,比K-Means、谱聚类、离散粒子群算法具有更好的聚类性能和全局收敛性.
英文摘要：
To improve the accuracy and efficiency of cancer gene expressing data clustering, Quantum- behaved particle swarm with comprehensive learning strategy（CLQPSO） and generalized regression neural network （GRNN） are studied, A cancer gene clustering algorithm was generated based on CLQPSO. GRNN takes advantage of the implicit rules in a number of similar genes and the prediction of missing val- ues for gene expression has higher credibility; CLQPSO algorithm can make full use of each particle best position and particle swarm social cooperation information offered, avoiding premature convergence in lo- cal optimum value. Experiments show that the integrated use of GRNN and CLQPSO algorithm has better clustering performance and global convergence compared with K-Means, spectral clustering, discrete par- ticle swarm algorithm in the aspect of cancer gene expressing data clustering.
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