A new frequent item set mining algorithm based on interval intersection

Frequent item set mining is an important data mining method with many real-life applications. This paper presents a new frequent item set mining algorithm based on interval intersection. For each item set in the mining dataset, an interval set is used to keep track of the transactions that contain this item set. Interval set intersection operations are then used to find the support counts of the itemsets. The experimental results showed that the proposed algorithm is faster than the bit table and the Apriori-TID algorithms on several experiments with different support counts, numbers of transactions, and average lengths of the transactions.