Comparative Performance Analysis of Postdiffset in Frequent vs. Infrequent Itemset Mining
-
https://doi.org/10.14419/ijet.v7i3.28.24683 -
Postdiffset, performance analysis, frequent itemset, infrequent itemset. -
Abstract
This paper presents comparative performance analysis of Postdiffset algorithm in mining of frequent and infrequent itemset via FIMI (Frequent Itemset Mining) benchmark case study. The Postdiffset is the Eclat-variant algorithm that mines the itemsets in tidsets (transaction id of items) format in the first looping and follows by diffset (difference set of itemsets) in the second looping onwards. We apply Postdiffset in mining of both frequent and infrequent itemset via dense datasets of chess and mushroom as well as for sparse datasets of retail and T10I4D100K. The overall results show postdiffset performs moderately between 21% to 40% towards tidset algorithm in frequent itemset mining in all datasets but loose performance towards diffset and sortdiffset. Contradictory, postdiffset gives promising results in terms of execution time with outperforming in all algorithms (diffset and sortdiffset) for all selected dense and sparse datasets between 23% to 99% outperformance percentage.
Â
Â
-
References
[1] Koh, Y. S., & Ravana, S. D. (2016). Unsupervised rare pattern mining: A survey. ACM Transactions on Knowledge Discovery from Data, 10(4), 45.
[2] Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th Int. Conf. Very Large Data Bases, 1215, 487-499.
[3] Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. In ACM Sigmod Record, 29(2), 1-12.
[4] Zaki, M. J., & Gouda, K. (2003). Fast vertical mining using diffsets. Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 326-335.
[5] Sareen, S., Gupta, S. K., & Sood, S. K. (2017). An intelligent and secure system for predicting and preventing Zika virus outbreak using Fog computing. Enterprise Information Systems, 11(9), 1436-1456.
[6] Shrivastava, S., & Johari, P. K. (2016, May). Analysis on high utility infrequent ItemSets mining over transactional database. Proceedings of the IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, pp. 897-902.
[7] Keste, P. A., & Shaikh, N. F. (2016). Improved approach for infrequent weighted itemsets in data mining. Proceedings of the IEEE International Conference on Electrical, Electronics, and Optimization Techniques, pp. 2663-2667.
[8] Jusoh, J. A., & Man, M. (2018). Modifying iEclat algorithm for infrequent patterns mining. Advanced Science Letters, 24(3), 1876-1880.
[9] Zaki, M. J. (2000). Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 12(3), 372-390.
[10] Kotiyal, B., Kumar, A., Pant, B., Goudar, R. H., Chauhan, S., & Junee, S. (2013). User behavior analysis in web log through comparative study of Eclat and Apriori. Proceedings of the IEEE 7th International Conference on Intelligent Systems and Control, pp. 421-426.
[11] Zheng, X., & Wang, S. (2014). Study on the method of road transport management information data mining based on pruning eclat algorithm and mapreduce. Procedia Social and Behavioral Sciences, 138, 757-766.
[12] Trieu, T. A., & Kunieda, Y. (2012). An improvement for declat algorithm. Proceedings of the ACM 6th International Conference on Ubiquitous Information Management and Communication, pp. 54.
[13] Abdullah, Z., Herawan, T., Ahmad, N., & Deris, M. M. (2011). Mining significant association rules from educational data using critical relative support approach. Procedia Social and Behavioral Sciences, 28, 97-101.
[14] Frequent Itemset Mining Repository at http://fimi.ua.ac.be/.
-
Downloads
-
How to Cite
Aezwani Wan Abu Bakar, W., Man, M., & Aida Jusoh, J. (2018). Comparative Performance Analysis of Postdiffset in Frequent vs. Infrequent Itemset Mining. International Journal of Engineering & Technology, 7(3.28), 144-148. https://doi.org/10.14419/ijet.v7i3.28.24683Received date: 2018-12-23
Accepted date: 2018-12-23