Cristian Bucila

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Constraint-based mining of itemsets for questions such as "find all frequent itemsets where the total price is at least $50" has received much attention recently. Two classes of constraints, monotone and antimonotone, have been identified as very useful. There are algorithms that efficiently take advantage of either one of these two classes, but no previous(More)
Often the best performing supervised learning models are ensembles of hundreds or thousands of base-level classifiers. Unfortunately, the space required to store this many classifiers, and the time required to execute them at run-time, prohibits their use in applications where test sets are large (e.g. Google), where storage space is at a premium (e.g.(More)
The subfield of itemset mining is essentially a collection of algorithms. Whenever a new type of constraint is discovered, a specialized algorithm is proposed to handle it. All of these algorithms are highly tuned to take advantage of the unique properties of their associated constraints, and so they are not very compatible with other constraints. In this(More)
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