Efficient Algorithm for Mining Probabilistic Frequent Itemsets of Uncertain Data

At present, uncertain data has become a hot research topic for scholars. For the current mining algorithms, there are still many shortcomings in terms of time and memory. How to improve or find efficient mining algorithms and quickly mine them Information that is more valuable to people has also become a thorny problem for researchers. At present, the existing algorithms for mining frequent itemsets of uncertain data probabilities with pattern growth have many shortcomings in terms of memory. Aiming at the problem that the existing PUFP-Growth algorithm consumes too much memory, this paper proposes a HUFP-Growth algorithm. This algorithm adds a candidate item set judgment mechanism to the original algorithm, which can judge the item set in advance Is it necessary to do linking to save a lot of memory. At the same time, it can also save the time consumed when the itemsets in the original algorithm are connected, and to a certain extent, it also saves the running time of the algorithm. Finally, the effectiveness of the method is proved through experiments.