Feasible itemset distributions in data mining: theory and application
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Tight upper bounds on the number of candidate patterns
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In the context of mining for frequent patterns using the standard level wise algorithm, the following question arises: given the current level and the current set of frequentpatterns, what is the maximal number of candidate patterns that can be generated on the next level? We answer this question by providing a tight upper bound, derived from a combinatorial result from the sixties by Kruskal andKatona. Our result is useful to educe the number of databasescans.