Abstract
To mine databases in which examples are tagged with class labels, the minimum correlation constraint has been studied as an alternative to the minimum frequency constraint. We reformulate previous approaches and show that a minimum correlation constraint can be transformed into a disjunction of minimum frequency constraints. We prove that this observation extends to the multi-class χ 2 correlation measure, and thus obtain an efficient new O(n) prune test. We illustrate how the relation between correlation measures and minimum support thresholds allows for the reuse of previously discovered pattern sets, thus avoiding unneccessary database evaluations. We conclude with experimental results to assess the effectivity of algorithms based on our observations.
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Nijssen, S., Kok, J.N. (2006). Multi-class Correlated Pattern Mining. In: Bonchi, F., Boulicaut, JF. (eds) Knowledge Discovery in Inductive Databases. KDID 2005. Lecture Notes in Computer Science, vol 3933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11733492_10
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DOI: https://doi.org/10.1007/11733492_10
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