基于FP-growth的大坝安全监测数据挖掘方法  

Data mining method for dam safety monitoring based on FP-growth algorithm

作　　者：毛宁宁 苏怀智 高建新[1,2] MAO Ningning;SU Huaizhi;GAO Jianxin(State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering,Hohai University,Nanjing 210098,China;College of Water Conservancy and Hydropower Engineering,Hohai University,Nanjing 210098,China)

机构地区：[1]河海大学水文水资源与水利工程科学国家重点实验室,江苏南京210098 [2]河海大学水利水电学院,江苏南京210098

出　　处：《水利水电科技进展》2019年第5期78-82,共5页Advances in Science and Technology of Water Resources

基　　金：国家重点研发计划(2018YFC0407101,2016YFC0401601);广西重点研发计划(桂科AB17195074)

摘　　要：为改善大坝安全监测数据库的数据挖掘方法运行速度慢、占用内存大的问题,提出改进FP-growth算法,将已预处理的监测数据剪枝后,生成Priority树再进行频繁项挖掘。以此方法挖掘大坝变形量与水温等环境量的相关关系,不仅挖掘速度快、精度高、结果简洁,还能够对比单个因子或分析多个因子耦合与目标变量的关系。实例表明改进后的FP-growth算法思想为大坝安全监测数据挖掘提供了一条良好的思路。In order to improve the current data mining method of the dam safety monitoring database which runs slowly and takes up a lot of computational space, a modified FP-growth algorithm was proposed. The pre-processed monitoring data was pruned, and then frequent item mining was performed after the Priority tree was generated. In the application of exploring the correlation between dam deformation and water temperature and other environmental quantities, the proposed method not only has high mining speed, high precision, and simple results, but also can compare a single factor or analyze the relationship between multiple factor coupling with target variables. The example shows that the improved FP-growth algorithm provides a good idea for dam safety monitoring data mining.

关 键 词：大坝安全监测 大坝变形分析 数据挖掘 关联法则 FP-GROWTH算法 

分 类 号：TV689.1[水利工程—水利水电工程]