关键词: 完全学习策略, 量子行为粒子群, 广义回归神经网络, 聚类

Abstract:

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 values 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 local 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 particle swarm algorithm in the aspect of cancer gene expressing data clustering.

Key words: comprehensive learning strategy, quantum-behaved particle swarm, generalized regression neural network, cluster