摘要
在一些大型的智能机械设备环境中,由于故障数据种类不断增加,形成了一个强冗余数据干扰的环境,这样的环境下,由于故障冗余关联规则的存在造成挖掘耗时。在充分研究关联挖掘算法的基础上,提出一种基于预测决策同态理论的强冗余数据挖掘算法,该算法通过对冗余关联中的数据以非同态信息增益惩罚因子构建同态区间,对区间内庞大的冗余关联数据进行关联约束,保证关联数据在距离较近的同态区间内,在邻近区间中采用预测决策方法进行故障的最终确认。实验证明,这种方法能够提高冗余环境下故障数据挖掘的准确率,其计算成本不高,鲁棒性较强。
In some large intelligent mechanical equipment environment, the fault data increases variety, forms a strong redundant interference environment, and in the environment, mining is time-consuming, because of the existence of unas- sociation rules. On the basis of full research of association mining algorithm, strong redundant data mining algorithm based on a prediction of the theory of decision homomorphism was proposed which constructs homomorphisms interval by the redundancy of the data with punish actor, constraints the interval of the huge redundant associated data correla- tion, ensures related data in the distance nears the homomorphism interval, and in the nearby interval, uses prediction methods of operation decision-making to make fault final confirmation. Experiments show that the method can improve the redundant environment, the accuracy of fault data mining, the calculation cost is not high, and it has a good robust- ness.
出处
《计算机科学》
CSCD
北大核心
2013年第7期232-235,共4页
Computer Science
基金
山西省2011年科学技术发展计划(20110321031)资助
关键词
预测决策
同态区间
数据挖掘
Forecast decision, Homomorphisms interval,Data mining
