This article presents a new interestingness measure for association rules called confidence gain (CG). Focus is given to extraction of human associations rather than associations between market products. There are two main differences between the two (human and market associations). The first difference is the strong asymmetry of human associations (e.g., the association “shampoo” → “hair” is much stronger than “hair” → “shampoo”), where in market products asymmetry is less intuitive and less evident. The second is the background knowledge humans employ when presented with a stimulus (input phrase). CG calculates the local confidence of a given term compared to its average confidence throughout a given database. CG is found to outperform several association measures since it captures both the asymmetric notion of an association (as in the confidence measure) while adding the comparison to an expected confidence (as in the lift measure). The use of average confidence introduces the “background knowledge” notion into the CG measure. Various experiments have shown that CG and local confidence gain (a low-complexity version of CG) successfully generate association rules when compared to human free associations. The experiments include a large-scale “free sssociation Turing test” where human free associations were compared to associations generated by the CG and other association measures. Rules discovered by CG were found to be significantly better than those discovered by other measures. CG can be used for many purposes, such as personalization, sense disambiguation, query expansion, and improving classification performance of small item sets within large databases. Although CG was found to be useful for Internet data retrieval, results can be easily used over any type of database.
The VLDB Journal – Springer Journals
Published: Jan 1, 2006
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