改进Apriori算法对试车台监测数据的关联挖掘  

Association mining of test bed monitor data based on improved Apriori algorithm

作　　者：刘自然[1] 王律强 李爱民[2] 张跃春 颜丙生[1] 甄守乐[1] 熊伟[1] 

机构地区：[1]河南工业大学机电工程学院,河南郑州450007 [2]河南工业大学工程训练中心,河南郑州450007 [3]中航工业湖南南方宇航高精传动有限公司,湖南株洲412002

出　　处：《中国测试》2015年第4期106-109,共4页China Measurement & Test

摘　　要：为发现某试车台中流量、压力等数据之间的关联关系,引入关联规则对监测数据进行挖掘,得到对提高试车安全监测性能具有较高的置信度和支持度的关联规则。提出改进的Apriori算法,对某型航空部件试车台试车过程的数据进行挖掘,与原方法相比,该方法效率高,在置信度和支持度相同的情况下,可以有效降低运算时间。In order to find the associated relation of flow,pressure variables in one type of test bed,association rules was introduced to mine the monitoring data,high degree of confidence and support association rules to improve the monitor performance of the test bed have been obtained.An application based on the data mining of testing course of one aircraft test bed indicate the proposed improved Apriori algorithm has better efficiency under the same confidence and support,which effectively reduce the computation time.

关 键 词：计算机应用 关联规则 数据挖掘 试车台 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]