基于灰色系统理论的快速时序数据挖掘算法  

Fast Time Sequential Data Mining Algorithm Based on Grey System Theory

作　　者：赵颖 ZHAO Ying(School of Continuing Education,Qinghai Open University,Xining 810000,China)

机构地区：[1]青海广播电视大学继续教育学院,西宁810000

出　　处：《西安文理学院学报（自然科学版）》2018年第5期40-43,共4页Journal of Xi’an University(Natural Science Edition)

摘　　要：快速时序算法是一种多目标优化的局部数据挖掘算法,处理繁杂的数据时容易陷入局部最优.为提高算法的全局数据挖掘能力,提出了一种基于灰色系统理论的快速时序数据挖掘算法.首先,该算法采用快速时序数据挖掘算法处理数据;然后,用灰色系统理论进行贪心迭代数据挖掘时,以求得更为理想的结果;最后,通过实验仿真,并与快速时序算法和MDO算法进行比较.结果表明,本文提出的数据挖掘算法具有更好的全局寻优能力,且数据挖掘效果更佳.Fast time ordinal number algorithm is a local data mining algorithm for multi-objective optimization,and it is easy to fall into local optimal when dealing with complicated data. In order to improve the global data mining ability of the algorithm,a fast time series data mining algorithm based on grey system theory is proposed. Firstly,the algorithm uses the fast time sequential data mining algorithm for data processing. Then,in order to get more ideal results,the grey system theory is used to do greedy iterative in the process of data mining. Finally,through the experiment simulation,and compared with the fast timing algorithm and the multidisciplinary design optimization（ MDO） algorithm,the results show that the data mining algorithm proposed in this paper has better global optimization ability,and the data mining effect is better.

关 键 词：快速时序 数据挖掘算法 灰色系统理论 局部最优 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]