摘要
关于股票准确预测问题,针对股票预测中计算时间复杂度大,数据库操作速度慢等缺点,为提高数据挖掘的速度和效率,提出了一种分割提速法的并行股票预测模型。模型特点采用广播拓扑结构,使用多线程并行计算方法,将计算量平均分配给所有参与计算的计算机,同时又使用网络编程技术实时同步回收结果,从而有效地缩短了股票关联规则的计算时间。实验结果表明,上述方法有效地减少了算法时间复杂度,较大程度地提高了股票预测的效率,从而为股票投资者提供有力的帮助。
To overcome the shortcomings such as large computation complexity in stock predication,slow database operation speed etc.and to improve the speed and efficiency of data mining,a parallel stock prediction model based on apriori partition speed method was proposed.It made good use of broadcasting topology architecture and multi-thread parallel computing,which can well-distribute the computing on multi-computers.Meanwhile,it gave real-time result collecting by using socket programming,thus reduced the computing time effectively.The experimental results show that the method reduces the time complexity of algorithm effectively,improves greatly the efficiency of stock predication,and offers a great help to the stock trader.
出处
《计算机仿真》
CSCD
北大核心
2012年第5期367-370,共4页
Computer Simulation
基金
河南省教育厅自然科学研究指导计划项目(2010C520007)
关键词
数据挖掘
关联规则
股票预测
并行计算
分割提速法
Data mining
Association rule
Stock prediction
Parallel computing
Partition speed method
