# An Asymptotically Tighter Bound on Sampling for Frequent Itemsets Mining

## TL;DR

This paper introduces a new, asymptotically tighter error bound for sampling in frequent itemsets mining, leading to simpler algorithms with guaranteed accuracy and improved efficiency.

## Contribution

It presents a novel, tighter error bound and a simplified approximation algorithm for frequent itemsets mining, enhancing accuracy and computational efficiency.

## Key findings

- New error bound is roughly half of existing bounds for small error probabilities.
- Simpler approximation algorithm with guaranteed worst-case error.
- Efficient algorithm for top-k frequent itemsets with high accuracy.

## Abstract

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.