摘要
针对基于基因表达式编程(GEP)的自动聚类算法GEP-Cluster中聚类中心的筛选和聚合、计算数据对象到各聚类中心距离两个关键步骤效率不高的问题,提出了一种基于统一计算设备架构(CUDA)和GEP的自动聚类改进算法(CGEP-Cluster)。CGEP-Cluster算法采用基因阅读运算器方法对GEP-Cluster算法的聚类中心筛选和聚合步骤进行改进,并基于CUDA将GEP-Cluster算法中数据对象到各聚类中心距离的计算并行化。实验结果表明,在数据对象规模较大时,CGEP-Cluster算法可获得8倍左右的加速比。CGEP-Cluster算法可用于聚类数未知且数据对象规模较大情况下的自动聚类。
There are two inefficient steps in GEP-Cluster algorithm: one is screening and aggregation of clustering centers and the other is the calculation of distance between data objects and clustering centers. To solve the inefficiency, an auto-clustering algorithm based on Compute Unified Device Architecture (CUDA) and Gene Expression Programming (GEP), named as CGEP-Cluster, was proposed. Specifically, the screening, and aggregation of clustering center step was improved by Gene Read & Compute Machine (GRCM) method, and CUDA was used to parallel the calculation of distance between data objects and clustering centers. The experimental results show that compared with GEP-Cluster algorithm, CGEP-Cluster algorithm can speed up by almost eight times when the scale of data objects is large. CGEP-Cluster can be used to implement automatic clustering with the clustering number unknown and large data object scale.
出处
《计算机应用》
CSCD
北大核心
2013年第7期1890-1893,1907,共5页
journal of Computer Applications
基金
福建省自然科学基金资助项目(2011J05146
2012J01250)
福建省杰出青年培育计划项目(福建省教育厅[2011]29号)
福建师范大学青年骨干教师培育计划项目(fjsdjk2012083)
福建省科技计划重大项目(2011H6006)
武汉大学软件工程国家重点实验室开放基金资助项目(SKLSE2012-09-28)
福建省教育厅科技项目(JA12077
JA12080
JB11028
JB11029)
关键词
统一计算设备架构
基因表达式编程
聚类算法
GEP
CLUSTER
演化算法
Compute Unified Device Architecture (CUDA) Gene Expression Programming (GEP) clustering algorithm GEP-cluster evolutionary algorithm