CAJ | 학술논문

电力通信网故障信息的频繁项集挖掘对于电力通信网故障诊断及排除具有重要意义.依据组合学基本原理和集合理论,提出一种将频繁项集挖掘转化成统计问题的算法.该算法利用Hadoop平台,只进行1次数据库扫描,即可完成频繁项集的挖掘;当改变支持度参数时无须重新挖掘.实验结果表明:该算法具有极好的扩展性;且对于大量事务数据,算法时间开销小,对于电力通信网故障诊断具有重要意义.
전력통신망고장신식적빈번항집알굴대우전력통신망고장진단급배제구유중요의의.의거조합학기본원리화집합이론,제출일충장빈번항집알굴전화성통계문제적산법.해산법이용Hadoop평태,지진행1차수거고소묘,즉가완성빈번항집적알굴;당개변지지도삼수시무수중신알굴.실험결과표명:해산법구유겁호적확전성;차대우대량사무수거,산법시간개소소,대우전력통신망고장진단구유중요의의.
How to mine the frequent itemsets of fault information in the power communication network is of great significance to the fault diagnosis and removal of the network. According to the basic theory of combinatorics and the set theory, a frequent itemsets mining algorithm is proposed, which transfers the issue of frequent itemsets mining into a statistical issue. And Hadoop platform is also used in the algorithm that scans the transaction database once to complete the target frequent itemsets mining, which needs no more scan or mining with support changing. The experimental results show that this algorithm performs well in expansibility and has a little time cost in handling large transaction database, which means a lot to the fault diagnosis and removal of power communication network.