The Association rule mining is one of the recent data mining research. Mining frequent itemsets in relational databases using relational queries give great attention to researchers nowadays. This paper implements modified set oriented algorithm for mining frequent itemsets in relational databases. In this paper, the sort and merge scan algorithm SETM (Houtsma and Swami, IEEE 25–33 (1995)) [1] is implemented for super market data set which is further improved by integrating transaction reduction technique. Our proposed algorithm Improved SETM (ISETM) generate the frequent itemsets from the database and find its execution time. Finally the performance of the algorithm is compared with the traditional Apriori and SETM algorithm.
CITATION STYLE
Kerana Hanirex, D., & Kaliyamurthie, K. P. (2016). Mining frequent itemsets in association rule mining using improved SETM algorithm. In Advances in Intelligent Systems and Computing (Vol. 394, pp. 765–773). Springer Verlag. https://doi.org/10.1007/978-81-322-2656-7_70
Mendeley helps you to discover research relevant for your work.