Abstract
One of the most well-studied problems in data mining is mining for association rules. There was also research that introduced association rule mining methods to conduct classification tasks. These classification methods, based on association rule mining, could be applied for customer segmentation. Currently, most of the association rule mining methods are based on a support-confidence structure, where rules satisfied both minimum support and minimum confidence were returned as strong association rules back to the analyzer. But, this types of association rule mining methods lack of rigorous statistic guarantee, sometimes even caused misleading. A new classification model for customer segmentation, based on association rule mining algorithm, was proposed in this paper. This new model was based on the support-significant association rule mining method, where the measurement of confidence for association rule was substituted by the significant of association rule that was a better evaluation standard for association rules. Data experiment for customer segmentation from UCI indicated the effective of this new model.
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References
Liu, B., Hsu, W., Ma, Y.M.: Integrating Classification and Association Rule Mining. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, New York, pp. 1–7 (1998)
Ha n, E.H., Karypis, G., Kumar, V.: Scalable Parallel Data Mining for Association Rules. Knowledge and Data Engineering. IEEE Transactions, pp. 337–352 (2000)
Han, J.W., Karmbr, M.: Data Mining: Concepts and Techniques, pp. 225–330. Morgan Kaufmann Publishers, San Francisco (2001)
Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules Between Sets of Items in Large Databases. In: Proc.1993 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’93), pp. 207–216. ACM Press, New York (1993)
Brin, S., Motwani, R., Silverstein, C.: Beyond Market Baskets: Generalizing Association Rules to Correlations. In: ACM SIGMOD Record, Proceedings of the ACM SIGMOD international conference on Management of data, pp. 265–276. ACM Press, New York (1997)
Ye, Q., Li, Y.J., Zhang, J.: Improved Method in Association Rule Mining. In: Proceeding of The 8th Asia Pacific Management Conference, pp. 1–8 (2002)
Adomavicius, G., Tuzbilin, A.: Using Data Mining Methods to Build Customer Profiles. Computer, 74–82 (2001)
Yin, X., Han J.: CPAR: Classification Based on Predictive Association Rules. In: Proceedings of the SDM, pp. 80–86 (2003)
Tsay, Y.J., Chiang, J.Y.: CBAR: An Efficient Method for Mining Association Rules. Knowledge Based Systems, pp. 432–444 (2005)
Mielikäinen, T.: Frequency-based Views to Pattern Collections. Discrete Applied Mathematics 154, 1113–1139 (2006)
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Li, G., Shi, W. (2007). New Classification Method Based on Support-Significant Association Rules Algorithm. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_51
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DOI: https://doi.org/10.1007/978-3-540-74205-0_51
Publisher Name: Springer, Berlin, Heidelberg
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