计算机科学 ›› 2013, Vol. 40 ›› Issue (7): 232-235.

• 人工智能 • 上一篇    下一篇

基于预测决策同态理论的故障数据优化挖掘算法

陆青梅,褚玉晓   

  1. 中北大学电子与计算机科学技术学院 太原030051;郑州大学西亚斯国际学院电子信息工程学院 郑州451150
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受山西省2011年科学技术发展计划(20110321031)资助

Fault Data Optimization Mining Algorithm Based on Theory of Prediction Decision Homomorphism

LU Qing-mei and CHU Yu-xiao   

  • Online:2018-11-16 Published:2018-11-16

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。

关键词: 预测决策,同态区间,数据挖掘 中图法分类号TP391.4文献标识码A

Abstract: 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 unassociation 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 factor,constraints the interval of the huge redundant associated data correlation,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 robustness.

Key words: Forecast decision,Homomorphisms interval,Data mining

[1] Su T,Dy J.A Deterministie Method for Initializing K-MeansClustering[C]∥Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence.Boca Raton, Florida,2009:784-786
[2] 齐继阳,竺长安.设备故障智能诊断方法的研究[J].仪器仪表学报,2009,27(10):34-36
[3] Cooley R,Mobasher B,Srivastava J.Grouping Web Page References into Transactions for Mining World Wide Web Browsing Patterns[R]. TR 97-021.University of Minnesota,Dept.of Computer Science,Minneapolis,2011:218-229
[4] 郭岩,白硕,于满泉.Web使用信息挖掘综述[J].计算机科学,2005,32(1):21-27
[5] Cooley R,Srivastava J.Grouping Web page references intotransactions for mining world wide Web browsing patterns[C]∥Proceedings of KDEX’97.NewportBeach,CA,USA,1997:2-7
[6] 田卫.RS-485总线分支线短路故障检测技术[J].微电子学与计算机,2011,28(4):116-117
[7] Mannila H,Toivonen H,Verkamo A I.Efficient algorithms for discovering association rules[C]∥KDD-94:AAAI Workshop on Knowledge Discovery in Database.Seattle,Washington,2009:181-192
[8] Buchner A G,Mulvenna M D.Discovering Internet MarketingIntelligence Through Online Analytical Web Usage Mining[J].SIGMOD Record,2008,27(4):54-61

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