Proportional fault-tolerant data mining with applications to bioinformatics
Information Systems Frontiers
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Mining frequent patterns is a fundamental and crucial task in data-mining problems. This paper proposes a novel and simple approach, which does not belong to the candidate generation-and-test approach (for example, the Apriori algorithm) and the pattern-growth approach (such as the FP-growth algorithm)two approaches. This approach treats the database as a stream of data and finds the frequent patterns by scanning the database only once. Two versions of the approach (i.e., mapping-table and transformation-function) are provided. Analyses and simulations of the approach are also performed. Analyses show that the transformation-function version is much better than the Apriori and FP-growth ones in storage complexity. Simulation results show that the mapping-table version is comparable to the FP-growth algorithm in execution time.