Stable Periodic Frequent Itemset Mining on Uncertain Datasets
Ruimeng He, Jinchao Chen, Chenglie Du, Yuxin Duan · 2021
In recent years, data mining has attracted great attention from the information industry. The main reason is that there is a large amount of data, which can be widely used, and there is an urgent need to convert these data into useful information and knowledge. Frequent itemset mining as an important research basis in data mining research has also been greatly developed. However, most of these frequent itemset mining algorithms only consider the frequency of itemsets and ignore the periodicity of itemsets, which makes the results incomplete. In order to solve the above problem, this paper presents a a stable periodic frequent itemset mining (SPFIM) algorithm on uncertain dataset, by considering both the frequency and periodicity of itemsets. The experimental evaluation on real datasets shows that the SPFIM algorithm is efficient and can find patterns that are not found by traditional algorithms.