In mining frequent Itemsets, Eclat algorithm is an important one. But it has some inefficiency. We proposed an algorithm called Improved_Eclat which is a new improved eclat method with high efficiency in the searching process to reduce the running time using two dimensional pattern tree. By comparing Improved_Eclat with Eclat , Eclat-opt and Bi-Eclat, hereby it is proved that the Improved_Eclat has the highest efficiency in mining associating rules from various databases.

Association rules, Eclat, increased search approach, increased two- dimensional pattern trees

[1] R. Agrawal, T. Imilienski and A. Swami, “Mining association rules between sets of items in large databases”, Proceeding of the ACM SIGMOD Int‟l Conference on Management of Data, Washington DC, (1993), pp. 207-216.
[2] J. Han, J. Pei and Y. Yin, “Mining frequent patterns without candidate generation”, Proceedings of the2000 ACM SIGMOD International Conference on Management of Data, (2000), pp. 1-12.
[3] J. Dong and M. Han, “BitTableFI: An efficient mining frequent itemsets algorithm”, Knowledge-Based Systems, vol. 20, no. 4, (2007), pp. 329-335.
[4] C. Aflori and M. Craus, “Grid implementation of the Apriori algorithm”, Advances in Engineering Software, vol.38, no. 5, (2007), pp. 295-300.
[5] F. Berzal, J. Cubero and N. Marin, “TBAR: An efficient method for association rule mining in relational databases”, Data & Knowledge Engineering, vol. 37, no. 1, (2001), pp. 47-64.
[6] D. C. Pi, X. L. Qin and N. S. Wang, “Mining Association Rule Based on Dynamic Pruning”, Mini- Micro Systems, vol. 10, (2004), pp. 1850-1852.
[7] J. Pork, M. Chen and P. Yu, “An effective hash based algorithm for mining association rules”, ACM SIGMOD, vol. 24, no. 2, (1995), pp. 175-186.
[8] S. Brin, R. Motwani and C. Silverstein, “Beyond market baskets: generalizing association rules to correlations,” ACM SIGMOD Conference on Management of Data, Tuscon, AZ, (1997), pp. 265–276.
[9] H. F. Li and S. Y. Lee, “Mining frequent itemsets over data streams using efficient window sliding techniques”, Expert Systems with Applications, vol. 36, no. 2, (2009), pp. 1466-1477.
[10] Y. F. Zhong and H. B. Lv, “An Incremental Updating Algorithm to Mine Association Rules Based on Frequent Pattern Growth”, Computer engineering and Application, vol. 26, (2004), pp. 174-175.
[11] R. Balazs, “Nonordfp: An FP-Growth Variation without Rebuilding the FP-Tree”, Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementa-tions, (2004).
[12] G. Gosta and J. F. Zhu, “Fast Algorithms for Frequent Itemset Mining Using FP-Trees”, IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 10, (2005), pp. 1347-1362.
[13] W. Ke, T. Liu, H. J. Wei and L. J. Qiang, “Top down fp-growth for association rule mining”, The 6th Pacific-Asia Conference, PAKDD 2002, Taipei, Taiwan, (2002), pp. 334-340.
[14] J. Pei, J. W. Han and H. J. Lu, “H-Mine: Fast and space-preserving frequent pattern mining in large databases”, IIE Transactions, vol. 39, no. 6, (2007), pp. 593-605.
[15] K. C. Lin, I. Liao and Z. S. Chen, “An improved frequent pattern growth method for mining association rules”, Expert Systems with Applications, vol. 38, no. 5, (2011), pp. 5154-5161.
[16] M. Zaki, “Scalable algorithms for association mining”, IEEE Transactions on Knowledge and Data Engineering, vol. 12, no. 3, (2000), pp. 372-390.
[17] L. S. Thieme, “Algorithmic Features of Eclat”, Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, Brighton, UK, November, (2004).
[18] M. J. Zaki and K. Gouda, “Fast vertical mining using diffsets”, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, New York, USA, (2003), pp. 326-335.
[19] X. Z. Yang, C. P. En and Z. Y. Fang, “Improvement of Eclat algorithm for association rules based on hash Boolean matrix”, Application Research of Computers, vol. 27, no. 4, (2010), pp. 1323-1325.
[20] F. P. En, L. Yu, Q. Q. Ying and L. L. Xing, “Strategies of efficiency improvement for Eclat algorithm”,Journal of Zhejiang University (Engineering Science), vol. 47, no. 2, (2013), pp. 223-230.
[21] Xiaomei Yu, Hong Wang, “Improvement of Eclat Algorithm Based on Support in Frequent Itemset Mining”,Journal ofComputers”, vol. 9, no. 9, (2014), pp. 2116-2123