摘要
描述了基于数据挖掘的通信网告警相关性分析。在分布式数据库中直接运用序列算法效率很低,因为这需要大量的额外通信。为此提出了一种有效的分布式关联规则挖掘算法——EDMA,它通过局部剪枝与全局剪枝来最小化候选项集数目和通信量。在局部站点上运用先进的压缩关联矩阵CMatrix统计局部项集支持数。此外还利用项目剪枝与交易剪枝共同来减少扫描时间。最后仿真验证了EDMA比其他经典分布式算法有更高的运算效率、更低的通信开销以及更好的可扩展性。
This paper described the alarm correlation in communication networks based on data mining. A direct application of sequential algorithms to distributed databases is not effective, because it requires a large amount of communication overhead. An efficient algorithm-EDMA was proposed. It minimized the number of candidate sets and exchanged messages by local and global pruning. In local sites, it runs the application based on the improved algorithm-CMatrix, which is used to calculate local support counts. Our solution also reduced the size of average transactions and datasets that leads to reduction of scan time. The performance study shows that EDMA has superior running efficiency, lower communication cost and stronger scalability than direct application of a sequential algorithm in distributed databases.
出处
《计算机科学》
CSCD
北大核心
2009年第11期204-207,212,共5页
Computer Science
基金
国家自然科学基金(60572091)资助
关键词
网络差错管理
分布式关联规则挖掘
频繁项集
压缩关联矩阵
Network fault management, Association rules distributed mining, Frequent itemsets, Compressed association matrix
