Abstract
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
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