An Improved Method for Counting Frequent Itemsets Using Bloom Filter

Frequent itemsets play an essential role in many data mining tasks that try to find interesting patterns from databases. Frequent itemset mining is one of the time consuming tasks in data mining. It is one of the prime steps in association rule mining. Many versions of frequent itemset mining algorithms have been proposed by many researchers that aim at reducing the time and space complexities. In this work we attempt to use bloom filter, a probabilistic data structure to determine the frequent itemsets. Bloom filter uses hashing to store data. Experiments on real datasets have shown that there is considerable advantage in terms of memory and performance in this technique compared to other hash based techniques.