Frequent pattern finding plays an essential role in mining associations, correlations and many more interesting relationships among data. Discovery of such correlations among huge amount of business transaction records can help in many aspects of business-related decision-making processes like catalog design, cross-marketing and customer shopping behavior analysis. “Market Basket Analysis” is one of such applications. It involves analysis of customer buying patterns by finding associations between the different items that customers place in their shopping carts. The discovery of such associations can help retailers and analysts to develop marketing strategies by gaining insight into which items are frequently purchased together by customers leading to increased sales by helping retailers do selective marketing and design efficient store layout.
Md. Mohsin, Md. Rayhan Ahmed, Tanveer Ahmed
Frequent Pattern Finding, Association Rules, Vertical Data Format, Closed Frequent Itemsets.
- R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proc. 1993 ACM SIGMOD Int. Conf. Management of Data (SIGMOD’93), pages 207–216, Washington, DC, May 1993.
- Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques, 2nd edition. Morgan Kaufmann, ISBN 978-1-55860-901-3
- R. Agrawal and R. Srikant. Fast algorithm for mining association rules in large databases. In Research Report RJ 9839, IBM Almaden Research Center, San Jose, CA, June 1994.
- A. Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. In Proc. 1995 Int. Conf. Very Large Data Bases (VLDB’95), pages 432–443, Zurich, Switzerland, Sept. 1995.
- J. S. Park, M. S. Chen, and P. S. Yu. An effective hash-based algorithm for mining association rules. In Proc. 1995 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’95), pages 175–186, San Jose, CA, May 1995.
- J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In Proc. 2000 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’00), pages 1–12, Dallas, TX, May 2000.
- M. J. Zaki. Scalable algorithms for association mining. IEEE Trans. Knowledge and Data Engineering, 12:372–390, 2000.
- "Apriori - Datasets". https://wiki.csc.calpoly.edu/. N.p., 2016. Web. 10 May 2016. Dataset source of transaction database.
|Published in :
||Volume 2 | Issue 3 | May-June - 2016
|Date of Publication
Cite This Article
Md. Mohsin, Md. Rayhan Ahmed, Tanveer Ahmed, "Closed Frequent Pattern Mining Using Vertical Data Format: Depth First Approach ", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 3, pp.230-238, May-June-2016.
URL : http://ijsrset.com/IJSRSET162366.php