Adapting the Right Measures for Pattern Discovery: A Unified View
Adapting the Right Measures for Pattern Discovery: A Unified View
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DOI：
10.1109/tsmcb.2012.2188283
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发表时间：
2012-08
期刊：
影响因子：
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通讯作者：
Junjie Wu;Shiwei Zhu;Hui Xiong;Jian Chen;Jianming Zhu
中科院分区：
文献类型：
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作者：
Junjie Wu;Shiwei Zhu;Hui Xiong;Jian Chen;Jianming Zhu
本文提出了一个统一的观点有趣的模式发现的有趣性措施。具体来说，我们首先提供三个必要条件的兴趣度措施被用于关联模式发现。然后，我们揭示了一个令人满意的属性的兴趣度措施：支持上升的条件反单调性（SA-CAMP）。沿着这条线，我们证明了具有SA-CAMP的措施是适合于模式发现，如果项目集遍历结构定义的支持上升集枚举树。此外，我们提供了一个全面的研究家庭的广义平均（GM）的措施，并显示其吸引人的属性，这是利用开发GMiner算法寻找有趣的关联模式。最后，实验结果表明，GMiner可以有效地识别有趣的模式的基础上SA-CAMP的GM措施，即使在极低的支持水平。
This paper presents a unified view of interestingness measures for interesting pattern discovery. Specifically, we first provide three necessary conditions for interestingness measures being used for association pattern discovery. Then, we reveal one desirable property for interestingness measures: the support-ascending conditional antimonotone property (SA-CAMP). Along this line, we prove that the measures possessing SA-CAMP are suitable for pattern discovery if the itemset-traversal structure is defined by a support-ascending set enumeration tree. In addition, we provide a thorough study on the family of the generalized mean (GM) measure and show their appealing properties, which are exploited for developing the GMiner algorithm for finding interesting association patterns. Finally, experimental results show that GMiner can efficiently identify interesting patterns based on SA-CAMP of the GM measure, even at an extremely low level of support.