Mining of Probabilistic Frequent Itemsets over Uncertain Data Streams

Frequent item sets mining algorithms in uncertain data streams almost base on the expected frequent item sets. Compared to probabilistic frequent item sets, it can´t reflect the confidence of item sets. We propose the algorithm based on probabilistic frequent item sets mining in uncertain data streams. The algorithm processes one basic sliding window every time, and the mining results are stored in the Probabilistic Frequent Tree. When the window sliding, it dynamically updates Probabilistic Frequent Tree to delete old data and add new data. Theoretical analysis and experiments show that the algorithm is effective.