  In this paper we present a new error bound on sampling algorithms for
frequent itemsets mining. We show that the new bound is asymptotically tighter
than the state-of-art bounds, i.e., given the chosen samples, for small enough
error probability, the new error bound is roughly half of the existing bounds.
Based on the new bound, we give a new approximation algorithm, which is much
simpler compared to the existing approximation algorithms, but can also
guarantee the worst approximation error with precomputed sample size. We also
give an algorithm which can approximate the top-$k$ frequent itemsets with high
accuracy and efficiency.
