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A Survey on Improving the Efficiency of Association Rule Mining Using FP-Growth

M. Banu Priya, V. Umarani


Data Mining is often considered as a process of automatic discovery of new knowledge from large databases. The extensive amount of knowledge and data stored in databases requires the development for storing and accessing the data, data analysis and effective use of stored knowledge of data. Association Rule Mining (ARM) is one of the important aspects in data mining, which generates large amount of itemsets in database. Many algorithms have been proposed to efficiently mine association rules. One of the most important approaches is the frequent pattern growth (FP-growth) method, which is efficient and scalable. In order to improve the efficiency of FP-growth method, this paper is surveyed on association rule mining with the aid of FP-growth algorithm in various aspects.


FP-Tree, Frequent Itemset Mining, Association Rule

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Agrawal, R, Imielinski, T, & Swami. A (1993), “Mining association rules between sets of items in large databases”, In Proceedings of the ACM SIGMOD conference on management of data (pp. 207–216).

Agrawal. R, & Srikant. R (1994),” Fast algorithm for mining association rules in large databases”, In Proceedings of 20th VLDB conference (pp. 487–499).

Chen-Feng Lee & Tsung-Hsien Shen(2005), “An FP-Split method for fast association rule mining”, IEEE, 0-7803-8932-8.

Grahne.G, & Zhu.J (2005),” Fast algorithms for frequent itemset mining using FP-trees”, IEEE Transactions on Knowledge and Data Engineering, 17(10), 1347–1362.

Go¨sta Grahne, and Jianfei Zhu (2005), “Fast Algorithms for Frequent Itemset Mining Using FP-Trees”, IEEE Transactions On Knowledge And Data Engineering, Vol. 17, No. 10.

Han.J, Pei.J & Yin.Y(2000), “Mining frequent patterns without candidate Generation”, In Proceedings of the ACM-SIGMOD conference management of data (pp. 1–12).

Han.J, Pei.J, Yin.Y & Mao.R (2004). “Mining frequent patterns without candidate generation: A frequent-pattern tree approach”, Data Mining and Knowledge Discovery, 8(1), 53–87.

Jiawei Han, Jian Pei, Yiwen Yin, Runying Mao (2001),” Mining Frequent Patterns without Candidate Generation: A Frequent Pattern Tree Approach*”, Data Mining and Knowledge Discovery, 8, 53–87.

Ke-Chung Lin, I-En Liao, Zhi-Sheng Chen(2011), “An improved frequent pattern growth method for mining association rules”, Expert Systems with Applications, 5154-5161.

Rezbaul Islam.A.B.M & Tae-sun Chung(2011), “An improved frequent pattern tree based association rule mining technique”, IEEE, 978-1-4244-9224-4.

Racz.B. (2004), “Nonordfp: An FP-growth variation without rebuilding the FP-tree”, In Proceedings of IEEE ICDM workshop on frequent itemset mining implementations.

Rakesh Agrawal, Tomasz Imielinski_ and Arun Swami (1993), “Mining Association Rules between Sets of Items in Large Databases”, PProceedings of the ACM SIGMOD Conference,Washington DC, USA.

Vaibhav Kant Singh, Vijay Shah, Yogendra Kumar Jain, Anupam Shukla, A.S. Thoke, Vinay Kumar Singh, Chhaya Dule, Vivek Parganiha (2008), ” Proposing an Efficient Method for Frequent Pattern Mining”, World Academy of Science, Engineering and Technology.

Wang, L. Tang, J. Han and J. Liu (2002), ”Top-Down FP-Growth for Association Rule Mining,” Lecture Notes in Computer Science ,Springer Berlin, Eidelberg Vol. 2336, pp..334 - 340.

Zaki M.J, Parthasarathy. S, Ogihara. M, Li. W (1997), ” New algorithms for fast discovery of association rules”, In Proceedings of 3rd knowledge discovery and data mining conference (pp. 283–286).


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