Abstract
In this work we discuss a supervised learning approach for identification of frequent itemsets and association rules from transactional data. This task is typically encountered in market basket analysis, where the goal is to find subsets of products that are frequently purchased in combination.
In this work we compare the traditional approach and the supervised learning approach to find association rules in a real-world retail data set using two well known algorithm, namely Apriori and PRIM.
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References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th VLDB Conference, pp. 487–499 (1994)
Bodon, F.: A survey on frequent itemset mining. Tech. rep., Budapest University of Technology and Economic (2006)
Brijs, T., Swinnen, G., Vanhoof, K., Wets, G.: Using association rules for product assortment decisions: A case study. In: Knowledge Discovery and Data Mining, pp. 254–260 (1999)
Friedman, J.H., Fisher, N.I.: Bump hunting in high-dimensional data. Statistics and Computing 9, 123–143 (1999)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery 8, 53–87 (2004)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning - Data Mining, Inference, and Prediction, 2nd edn. Springer, Heidelberg (2009)
R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2011), ISBN 3-900051-07-0, http://www.R-project.org ,
Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., Zhou, Z.H., Steinbach, M., Hand, D.J., Steinberg, D.: Top 10 algorithms in data mining. Knowledge Information Systems 14, 1–37 (2007)
Zaki, M.J.: Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering 12(3), 372–390 (2000)
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Kronberger, G., Affenzeller, M. (2012). Market Basket Analysis of Retail Data: Supervised Learning Approach. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2011. EUROCAST 2011. Lecture Notes in Computer Science, vol 6927. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27549-4_59
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DOI: https://doi.org/10.1007/978-3-642-27549-4_59
Publisher Name: Springer, Berlin, Heidelberg
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