A Synthetically Improved Method Based on Apriori Algorithm

Abstract:

There are some disadvantages of the Apriori algorithm，such as too many scan of the database and many redundant middle itemsets to be generated. In this paper, we propose an improved algorithm, OApriori, with a synthetical method: (1) pruning strategy, (2) connection strategy, (3) reducing the scanning scale of database. We have performed extensive experiments and compared the performance of two algorithms. It was found that the improved algorithm reduces the counts of unnecessary candidate itemsets, accelerates the speed of the algorithm.

• Periodical: Advanced Materials Research (Volumes 546-547)
• Pages: 1495-1500
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.546-547.1495
• Online since: July 2012
• Authors: Min Zhang, Yu Hou, Liang Wei Yao
• Keywords: Association Rule, Connection Strategy, Data Mining (DM), Frequent Item Set, Pruning Strategy

References

[1] Agrawal, R., Imielinski, T., and Swami, A.: Mining association rules between sets of items in large databases. Proceedings of ACM SIGMOD Conference on Management of Data, 1993 (SIGMOD'93), 207-216.

DOI: 10.1145/170035.170072

[2] Vaibhav Sharma, M. M. Sufyan Beg: A Probabilistic Approach to Apriori Algorithm. IEEE International Conference on Granular Computing - GRC , (2010)402-408.

DOI: 10.1109/grc.2010.69

[3] Sean Chester, Ian Sandler, Alex Thomo: Scalable APRIORI-Based Frequent Pattern Discovery. IEEE International Conference on Computational Science and Engineering - CSE, (2009)48-55.

DOI: 10.1109/cse.2009.51

[4] Lamine M. Aouad, Nhien-An Le-Khac, Tahar M. Kechadi: Performance study of distributed Apriori-like frequent itemsets mining. Knowledge and Information Systems - KAIS, vol. 23, no. 1, (2010)55-72.

DOI: 10.1007/s10115-009-0205-3

[5] Stanley B. Lippman, Josée LaJoie, Barbara E. Moo: C++ Primer (4th Edition). London, Addison Wesley/Pearson, (2006)377-433.

[6] Zhangyan Xu, Meiling Liu, Shichao Zhang, Jingli Lu, Yuming Ou: Three Optimized Methods of Apriori Algorithm. Computer Engineering and Application, 40(36). (2004)190-192.

[7] Frequent Itemset Mining Implementations Repository. http: /fimi. ua. ac. be/data.

[8] Yang, Ruixin, Tang, Jiang, Sun, Donglian: Association Rule Data Mining Applications for Atlantic Tropical Cyclone Intensity Changes. Weather and Forecasting, vol. 26, issue 3, (2011)337-353.

DOI: 10.1175/waf-d-10-05029.1

[9] Ping Chen, Garcia, W.: Hypothesis Generation and Data Quality Assessment through Association Mining. The 9th IEEE International Conference on Cognitive Informatics. (2010)659 - 666.

DOI: 10.1109/coginf.2010.5599828

[10] R. Forsati, M.R. Meybodi: Effective page recommendation algorithms based on distributed learning automata and weighted association rules. Expert Systems with Applications, Volume 37, Issue 2, (2010)1316-1330.

DOI: 10.1016/j.eswa.2009.06.010