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A Comparative Study of Pattern Recognition Algorithms on Sales Data
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 141 - Number 1 |
| Year of Publication: 2016 |
| Authors: Maulik Shah, Nirali Shah, Anviksha Shetty, Darshan Shah, Pradnya Gotmare |
10.5120/ijca2016909463
|
Maulik Shah, Nirali Shah, Anviksha Shetty, Darshan Shah, Pradnya Gotmare . A Comparative Study of Pattern Recognition Algorithms on Sales Data. International Journal of Computer Applications. 141, 1 ( May 2016), 38-41. DOI=10.5120/ijca2016909463
Abstract
In the realm of Data Mining looking for patterns and association rules is a very critical task and has been widely studied in the past years. There exist several data mining algorithms to find Association Rules in given datasets. One of the most popular and widely used algorithm is the Apriori algorithm to find patterns and itemsets in huge datasets and getting the association rules between them. This is done to gather knowledge from otherwise unsuspecting and random data. The Fp-Growth algorithm is similarly a different algorithm which uses an extended frequent pattern prefix-tree data structure for storing critical data after compression about frequent pairs. In this paper we do a comparative analysis of the 2 most popular pattern recognition algorithms and their performance on sales data of a college canteen sales transnational database where each record consists of items purchased by customer.
References
- M. Halkidi, “Quality assessment and uncertainty handling in data mining process,” in Proc, EDBT Conference, Konstanz, Germany, 2000.
- U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From data mining to knowledge discovery in databases,” AI magazine, vol. 17, no. 3, p. 37, 1996.
- Fayyad, U. (1997). Data Mining and Knowledge Discovery in Database: Implications from Scientific Database (pp.2-11), Washington USA.
- R. Agrawal, R. Srikant, “Fast algorithms for mining association rules”, Proceedings of the 20th Very Large DataBases Conference (VLDB’94), Santiago de Chile, Chile, 1994, pp. 487-499.
- Chen, Y.L ,K.Tang.R.J. Shen and Y.H.Hu (2005). Market Basket Analysis in a multiple store environment, Decision Support Systems 40(2):339-54. Retrived on April 2, 2012 from http://cqx.sagepub.com/centent/51/4/492
- Kamber, Jiawei Han (2006). Data Mining Concepts and Techniques, 2nd edition Elsevier publications. Retrieved on May 14, 2012 from http://www.elsevier.com/locate/dsw/pdf
- Srikant,R. (1994). Fast Algorithm for Mining Association Rules in Large Database. Proc Int. Conf. on Very Large Database (pp.478-499). Santiago, Chile.
- Teng, C.M. (2003). A Compariosn of Standard and Interval Association Rules. In Proceedings of the Sixteenth International FLAIRS Conference (pp. 371-375).
- Ping Ho Ting, Steve Pan & Shou Shiung Chou. (2010). Finding Ideal Menu Items Assortments. An Empirical Application of Market Basket Analysis.SAGE.
- Pardoe, I. (2008). Data mining techniques:Market basket analysis rules.
- LimitedBrands (2004). Achieving Greater Efficiencies with Market Basket Analysis, Microstrategy World 2004 Conference, Miami.
- Bo Wu; Defu Zhang; Qihua Lan; Jiemin Zheng, "An Efficient Frequent Patterns Mining Algorithm Based on Apriori Algorithm and the FP-Tree Structure," in Convergence and Hybrid Information Technology, 2008. ICCIT '08. Third International Conference on , vol.1, no., pp.1099-1102, 11-13 Nov. 008 doi: 10.1109/ICCIT.2008.109
- J.Han, J.Pei and Y.Yin., “Mining frequent patterns without candidate Generation”, in: Proceeding of ACM SIGMOD International Conference Management of Data, 2000, pp. 1-12.
- http://www.sqldatamining.com/wpcontent/uploads/2012/11/Steps-of-the-Knowledge-Discovery-in-Databases-Process.jpg
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