基于SnownLp和FP-Tree的操作票考核系统任务筛选研究
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摘要
火力发电是我国主要的电力生产方式,火电厂的安全运行需要员工对各项操作熟练掌握。电厂"两票"作为操作准确的重要保证,要求员工熟练使用。针对电厂当前"两票"练习系统只能检索特定任务的问题,提出基于中文分词和关联规则挖掘的方法。使用Snown Lp工具对票任务文本内容进行中文分词处理并提取关键词,建立对应的票任务关键词库,基于关键词库使用FP-Tree算法进行关联规则挖掘。试验结果表明,本方法可以有效地筛选出与给定的票任务类似票,有效提高练习的针对性,增强练习效果。
Thermal power generation is the primary electrical power source in China.The safe operation of thermal power plants requires employees to master all operations.The "two votes" of the power plant is an important guarantee for ensuring the accuracy of operations,and employees are required to use it skillfully.For the current "two votes" exercise system of the power plant,only the task can be retrieved,and a method based on Chinese word segmentation and association rule mining is proposed.Firstly,the SnownLp tool is used to process the word segmentation of the ticket task and extract the keywords.Then the keyword library is built.Finally,the association rules mining is performed using the FP-Tree algorithm based on the keyword library.The test results show that this method can effectively filter out tickets similar to the given ticket task.
Thermal power generation is the primary electrical power source in China.The safe operation of thermal power plants requires employees to master all operations.The "two votes" of the power plant is an important guarantee for ensuring the accuracy of operations,and employees are required to use it skillfully.For the current "two votes" exercise system of the power plant,only the task can be retrieved,and a method based on Chinese word segmentation and association rule mining is proposed.Firstly,the SnownLp tool is used to process the word segmentation of the ticket task and extract the keywords.Then the keyword library is built.Finally,the association rules mining is performed using the FP-Tree algorithm based on the keyword library.The test results show that this method can effectively filter out tickets similar to the given ticket task.
引文
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[2]王召鹏.基于Java EE的电厂两票系统的设计与开发[D].北京:华北电力大学,2015.
[3]林景亮.关联规则挖掘算法及其应用研究[D].厦门:厦门大学,2007.
[4]陈卓民.数据挖掘技术在国内外的研究和发展现状[J].青年文学家,2009(16):122-123.
[5]王爱平,王占凤,陶嗣干,等.数据挖掘中常用关联规则挖掘算法[J].计算机技术与发展,2010,20(4):105-108.
[6]毛国君.数据挖掘技术与关联规则挖掘算法研究[D].北京:北京工业大学,2003.
[7]何波.基于频繁模式树的分布式关联规则挖掘算法[J].控制与决策,2012,27(4):618-622.
[8]Hong T P,Lin C W,Wu Y L.An Efficient FUFP-tree Maintenance Algorithm for Record Modification[J].International Journal of Innovative Computing Information&Control Ijicic,2008,4(11).
[9]付冬梅,王志强.基于FP-tree和约束概念格的关联规则挖掘算法及应用研究[J].计算机应用研究,2014,31(4):1 013-1 015.
[10]SUI Y,SHAO F J,SUN R C,et al.A sequential pattern mining al gorithm based on improved FP-tree[C].International Conference on Software Engineering,Artificial Intelligece,Networking and Parallel/Distributed Computing,2008.
[11]ROBIN M T I,RASEL A A S,SIDDIKA A.An efficient approach to mine frequent itemsets using the variant of classic apriori and FP-Tree[C].IJSRSET,2017.
[12]刘华,张亚昕.基于FP-tree算法的推荐系统设计与实现[J].电子设计工程,2015,23(2):81-83.