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基于贝叶斯网络的频繁模式兴趣度计算及剪枝 被引量：4

Computing and Pruning Method for Frequent Pattern Interestingness Based on Bayesian Networks
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摘要 采用贝叶斯网络表示领域知识,提出一种基于领域知识的频繁项集和频繁属性集的兴趣度计算和剪枝方法 BN-EJTR,其目的在于发现与当前领域知识不一致的知识,以解决频繁模式挖掘所面临的有趣性和冗余问题.针对兴趣度计算过程中批量推理的需求,BN-EJTR提供了一种基于扩展邻接树消元的贝叶斯网络推理算法,用于计算大量项集在贝叶斯网络中的支持度;同时,BN-EJTR提供了一种基于兴趣度阈值和拓扑有趣性的剪枝算法.实验结果表明,与同类方法相比,方法 BN-EJTR具有良好的时间性能,而且剪枝效果明显;分析发现,经过剪枝后的频繁属性集和频繁项集相对于领域知识符合有趣性要求. Based on background knowledge represented as a Bayesian network, this paper presents a BN-EJTR method that computes the interestingness of frequent items and frequent attributes, and prunes. BN-EJTR seeks to find inconsistent knowledge relative to background knowledge and to resolve the problems of un-interestingness and redundancy faced by frequent pattern mining. To deal with the demand of batch reasoning in Bayesian networks during computing interestingness, BN-EJTR provides a reasoning algorithm based on extended junction tree elimination for computing the support of a large number of items in a Bayesian network. In addition, BN-EJTR is equipped with a pruning mechanism based on a threshold for topological interestingness. Experimental results demonstrate that BN-EJTR has a good time performance compared with the same classified methods, and BN-EJTR also has effective pruning results. The analysis indicates that both the pruned frequent attributes and the pruned frequent items are un-interesting in respect to background knowledge.
作者 胡春玲 吴信东 胡学钢 姚宏亮
机构地区 合肥工业大学计算机与信息学院 合肥学院网络与智能信息处理重点实验室
出处 《软件学报》 EI CSCD 北大核心 2011年第12期2934-2950,共17页 Journal of Software
基金 国家自然科学基金(60828005 60975034 61070131)
关键词 频繁模式 贝叶斯网络 邻接树 兴趣度 剪枝 frequent pattern Bayesian network junction tree interestingness pruning
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献21

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共引文献78

同被引文献25

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二级引证文献49

软件学报
2011年 第12期

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