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A Boolean Approach for Detection of Frequent Items

We propose a new technique for identifying frequent patterns that occur in large transactional data sets using Boolean algebras. Our approach, which involves extending the notion of support from sets of attributes to Boolean functions is more expressive than the standard approach that amounts to a search for conjunctive patterns. An efficient algorithm based on computing supports for minterms is presented. Also, we discuss an application of this algorithm to finding independent sets in graphs.