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Abstract
Abstract: - Association rule mining explores interesting relationships among items in a given data set. An objective of association rule mining is to develop a systematic method using the given database and finds relationships between the different items. Goal of association rules finding associations among items from a set of transactions, which contain a set of items. In this paper we focused on explaining the fundamentals of association mining and analyze implementations of the well-known association rule algorithms. Study focuses on algorithms Apriori, FP-Growth, and Dynamic Itemset Counting. Moreover, the algorithm generates frequent item sets in order so that the result can be used expediently.
Key Terms – Association Rule Mining, FP-Growth Algorithm, Apriori algorithm, Frequent Itemsets.##plugins.themes.academic_pro.article.details##
How to Cite
Anju Gandhi, N. (2014). Research of Improved Association Rule Algorithms (Apriori and FP-Growth). International Journal of Emerging Trends in Science and Technology, 1(06). Retrieved from https://igmpublication.org/ijetst.in/index.php/ijetst/article/view/294
References
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2. R. Agrawal and R. Srikant. “Fast algorithms for mining association rules.â€, VLDB'94 487-499, Santiago, Chile.
3. S. Brin, R. Motwani, and C. Silverstein. “Beyond market basket: Generalizing association rules to correlationsâ€, SIGMOD'97, 265-276, Tucson, Arizona.
4. S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. “Dynamic itemset counting and implication rules for market basket analysisâ€, SIGMOD'97, 255-264, Tucson, Arizona, May 1997.
5. J. Han, J. Pei, and Y. Yin. “Mining frequent patterns without candidate generationâ€, SIGMOD'00, 1-12, Dallas, TX, May 2000.
2. R. Agrawal and R. Srikant. “Fast algorithms for mining association rules.â€, VLDB'94 487-499, Santiago, Chile.
3. S. Brin, R. Motwani, and C. Silverstein. “Beyond market basket: Generalizing association rules to correlationsâ€, SIGMOD'97, 265-276, Tucson, Arizona.
4. S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. “Dynamic itemset counting and implication rules for market basket analysisâ€, SIGMOD'97, 255-264, Tucson, Arizona, May 1997.
5. J. Han, J. Pei, and Y. Yin. “Mining frequent patterns without candidate generationâ€, SIGMOD'00, 1-12, Dallas, TX, May 2000.