NOV-CFI: A Novel Algorithm for Closed Frequent Itemsets Mining in Transactional Databases

Since the era of data explosion, data mining in transactional databases has become more and more important. There are many data mining techniques like association rule mining, the most important and well-researched one. Furthermore, closed frequent itemset mining is one of the fundamental but time-consuming steps in association rule mining. Most of the algorithms used in literature find closed frequent itemsets on search space items having at least a minsup and are not reused for mining next time. To deal with this problem, NOV-CFI algorithms are proposed as a new approach in order to quickly detect closed frequent itemsets from transactional databases using an array of co-occurrences and occurrences of kernel item in at least one transaction. Advantages of NOV-CFI algorithms are reuse and easily expanded in distributed systems. Finally, experimental results show that the proposed algorithms are better than other existing algorithms on both real and synthetic datasets.