Computer Science – Databases
Scientific paper
2012-03-20
Computer Science
Databases
14 pages, 4 pdf figures
Scientific paper
Computing frequent itemsets in transactional databases is a vital but computationally expensive task. Measuring the difference of two datasets is often done by computing their respective frequent itemsets despite high computational cost. We propose a linear programming-based approach to this problem and show that there exists a distance measure for transactional database that relies on closed frequent itemsets but does not require their generation. We also prove that classification of datasets up to their frequent itemsets is a hopeless task since well-described canonical forms do not exist for this problem.
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