支持向量数据描述的基因表达数据聚类方法  

Improved gene expression data clustering using a support vector domain description algorithm

作　　者：季瑞瑞[1] 刘丁[1] 

机构地区：[1]西安理工大学信控中心,陕西西安710048

出　　处：《智能系统学报》2009年第6期544-548,共5页CAAI Transactions on Intelligent Systems

摘　　要：为改善传统的基因表达数据聚类方法正确率偏低的问题,研究了支持向量数据描述(SVDD)算法在基因表达数据聚类中的应用,该方法通过寻找最优分类超球实现对数据集的有效聚类.将类间信息融入聚类有效性评估准则中,通过模拟退火优化算法寻找SVDD算法中的最优核函数参数和惩罚因子,在训练时引入非样本数据提高运算效率.对酵母细胞生长周期的基因表达数据集的仿真实验结果表明,在新的聚类有效性评估准则下进行参数寻优,能够更快更好地得到最佳参数,同时,算法具有聚类精度高和运算速度快的优点.The application of the support vector domain description（SVDD） algorithm in gene expression data clustering was proposed as a means to improve the low accuracy of current clustering methods.This method effectivly clustered the dataset by finding the optimal separating hyper-sphere.Inter-class information was introduced into the current clustering assessment criterion in the form of a minimum within-class distance.The simulated annealing（SA） algorithm was used to find the optimal kernel function parameter and the punishment factor of the SVDD algorithm.Non-sample data were added in training to increase computational efficiency.Simulation results using the yeast cell cycle expression dataset showed that optimal parameters can be obtained faster and more accurately with the new assessment criteria.Similar improvements were found in clustering accuracy and speed.

关 键 词：基因表达数据 支持向量数据描述 聚类 模拟退火 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]