Adapting the Right Measures for Pattern Discovery: A Unified View

作者：Wu, Junjie; Zhu, Shiwei^*; Xiong, Hui; Chen, Jian; Zhu, Jianming

来源：IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics , 2012, 42(4): 1203-1214.

DOI：10.1109/TSMCB.2012.2188283

• Conditional antimonotone property (AMP)
• correlation computation
• generalized mean (GM)
• interestingness measure
• set enumeration tree (SET)

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.

• 出版日期2012-8
• 单位北京航空航天大学; 中央财经大学; rutgers; 清华大学