Abstract
Contrast set mining has been well-studied to detect the change between several contrasted databases. In the previous studies, they compared the supports of an itemset and extracted the itemsets with significantly different supports across those databases. Differently, we contrast the correlations of an itemset between two contrasted databases and try to detect potential changes. Any highly correlated itemset is out of our concern in order to focus on implicitly emerging correlation. Therefore, we set correlation constraints (upper bounds) in both databases, and then extract the itemsets consisting of items that are not highly correlated in both databases, but having a significant change of correlations from one database to the other. We regard both of positive and negative correlation. We also consider correlated itemsets under conditioning by third variables. Thus so called partial correlation is also regarded. To cover the correlation notion, we use extended mutual information. In our search procedure for the correlated itemsets, we use double clique condition that is necessary for itemsets to be solutions satisfying the correlation constraints. We show its usefulness by some experiments.
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Li, A., Haraguchi, M., Okubo, Y. (2011). Contrasting Correlations by an Efficient Double-Clique Condition. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2011. Lecture Notes in Computer Science(), vol 6871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23199-5_35
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DOI: https://doi.org/10.1007/978-3-642-23199-5_35
Publisher Name: Springer, Berlin, Heidelberg
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