HPM-FSI: A High-Performance Algorithm for Mining Frequent Significance Itemsets
In the traditional frequent itemsets mining on transactional databases, which items have no weight (equal weight, as equal to 1). However, in real world applications are often each item has a different weight (the importance/significance of each item). Therefore, we need to mining weighted/significance itemsets on transactional databases. In this paper, we propose a high-performance algorithm called HPM-FSI for mining frequent significance itemsets based on approach NOT satisfy the downward closure property (a great challenge). The experimental results show that the proposed algorithms perform better than other existing algorithms on both real-life and synthetic datasets.
KeywordsFrequent significance itemsets High-performance HPM-FSI algorithm NOT satisfy the downward closure property
This work was supported by University of Social Sciences and Humanities; University of Science, VNU-HCMC, Ho Chi Minh City, Vietnam.
- 1.Agrawal, R., Imilienski, T., Swami, A.: Mining association rules between sets of large databases. In: ACM Sigmod IC on Management of Data, Washington, DC, pp. 207–216 (1993)Google Scholar
- 3.Huai, Z., Huang, M.: A weighted frequent itemsets incremental updating algorithm base on hash table. In: 3rd International Conference on Communication Software and Networks (ICCSN), pp. 201–204. IEEE (2011)Google Scholar
- 4.Cai, C.H., Fu, A.W., Cheng, C.H., Kwong, W.W.: Mining association rules with weighted items. In: Proceedings of International Database Engineering and Applications Symposium (IDEAS 1998), pp. 68–77 (1998)Google Scholar
- 5.Tao, F., Murtagh, F., Farid, M.: Weighted association rule mining using weighted support and significance framework. In: SIGKDD 2003, pp. 661–666 (2003)Google Scholar
- 7.Phan H., Le B.: A novel parallel algorithm for frequent itemsets mining in large transactional databases. In: Perner P. (eds.) Advances in Data Mining. Applications and Theoretical Aspects, ICDM 2018. Lecture Notes in Computer Science, vol. 10933, pp. 272–287. Springer, Cham (2018)CrossRefGoogle Scholar