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数据挖掘中一种基于集合覆盖的元素重要性估计算法

An Importance of Elements Estimation Algorithm Based on Set-cover in Data Mining
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摘要 对给定数据集合的元素重要性进行估计是数据挖掘领域中的一项重要应用。现有的技术都是通过排序或选择来发现重要元素,其主要缺点是没考虑高排名对象可能非常相似甚至完全相同这一事实,忽略了高排名对象间的冗余性。因此,在强调多样性的场合,该方法性能有限。通过将排序和选择相结合,提出一种基于集合覆盖的元素重要性估计算法。该算法不仅考察单个集合覆盖的解,而且计算元素参与的高质量集合覆盖数量,进而为元素分配重要性分值。基于实际数据的实验和用户学习结果表明,算法性能高效,元素重要性评估结果的有用性高,且与人类感知相一致。 Iteratively reweighted leassquare(IRLS) method iused to calculate two consecutive image optical flow, According to the results, the motion state of the video surveillance sceneiestimated, and divided them into static, general sportand strenuouexercise. Then differenbistream storage strategy iused to save the differenscenes. Experimentshow thathe method can recognize and classify the common motobehavioof the monitoring scene with robustness. Thimethod can reduce the redundanvideo frame storage resource occupation, and expand the storage capacity of sensitive video data.
作者 薛若雯 陈刚
出处 《科学技术与工程》 北大核心 2013年第33期10003-10012,共10页 Science Technology and Engineering
关键词 数据挖掘 元素重要性 排序 选择 集合覆盖 分值 optical flow iteratively reweighted least squares motion analysis intelligent stream
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