Sliding Window- based Frequent Itemsets Mining over Data Streams using Tail Pointer Table

Mining frequent itemsets over transaction data streams is critical for many applications, such as wireless sensor networks, analysis of retail market data, and stock market predication. The sliding window method is an important way of mining frequent itemsets over data streams. The speed of the sliding window is affected not only by the efficiency of the mining algorithm, but also by the efficiency of updating data. In this paper, we propose a new data structure with a Tail Pointer Table and a corresponding mining algorithm; we also propose a algorithm COFI2, a revised version of the frequent itemsets mining algorithm COFI (Co-Occurrence Frequent-Item), to reduce the temporal and memory requirements. Further, theoretical analysis and experiments are carried out to prove their effectiveness.