Association Mining in Large Databases: A Re-examination of Its Measures

Abstract. In the literature of data mining and statistics, numerous interestingness measures have been proposed to disclose succinct object relationships of association patterns. However, it is still not clear when a measure is truly effective in large data sets. Recent studies have identified a critical property, null-(transaction) invariance, for measuring event associations in large data sets, but many existing measures do not have this property. We thus re-examine the null-invariant measures and find interestingly that they can be expressed as a generalized mathematical mean, and there exists a total ordering of them. This ordering provides insights into the underlying philosophy of the measures and helps us understand and select the proper measure for different applications.

• Type: Conference
• Year: 2007
• Where: PKDD
• Authors: Tianyi Wu, Yuguo Chen, Jiawei Han
• Keywords: Large Data Sets, Numerous Interestingness Measures, PKDD 2007, Succinct Object Relationships