Probabilistic Frequent Itemset Mining Algorithm over Uncertain Databases with Sampling

Abstract

Uncertain data is the data accompanied with probability, which makes the frequent itemset mining have more challenges. Given the data size n, computing the probabilistic support needs O(n(logn)2) time complexity and O(n) space complexity. This paper focuses on the problem of mining probabilistic frequent itemsets over uncertain databases and proposed PFIMSample algorithm. We employ the Chebyshev inequation to estimate the frequency of the items, which decreases certain computing from O(n(logn)2) to O(n). In addition, we propose the sampling technique to improve the performance. Our extensive experimental results show that our algorithm can achieve a significantly improved runtime cost and memory cost with high accuracy.