NOV-MFI: A Novel Algorithm for Maximal Frequent Itemset Mining
Huan Phan
International Journal of Computational Linguistics Research, Vol. 11, No. 2, 2020.
DOI: https://doi.org/10.6025/jcl/2020/11/2/60-72
Link: http://www.dline.info/jcl/fulltext/v11n2/jclv11n2_2.pdf
Abstract:
Since data explosion, data mining in transactional databases are increasingly important. There are many techniques for data mining such as mining association rule, the most important and well-researched. Moreover, maximal frequent itemset mining is one of the basic but time-consuming steps in the mines of association rules. Most algorithms used in the literature find maximal frequent itemset on search space items that have support at least minsup and not be used again for mining. In this paper, we propose a novel algorithm called NOV-MFI for mining maximal frequent itemsets in transactional databases. Advantages of NOV-MFI 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.