Positive and negative association mining are well-known and
extensively studied data mining techniques to analyze market basket
data. Efficient algorithms exist to find both types of association, separately or simultaneously. Association mining is performed by operating on the transaction data. Despite being an integral part of the transaction data, the pricing and time information has not been incorporated into market basket analysis so far, and additional attributes have been handled using quantitative association mining. In this paper, a new approach is proposed to incorporate price, time and domain related attributes into data mining by re-mining the association mining results. The underlying factors behind positive and negative relationships, as indicated by the association rules, are characterized and described through the second data mining stage re-mining. The applicability of the methodology is demonstrated by analyzing data coming from apparel retailing industry, where price markdown is an essential tool for promoting sales and generating increased revenue.
Demiriz, A., Ertek, G., Atan, T., and Kula, U. (2010) “Re-mining Positive and Negative AssociationMining Results” P. Perner (Ed.): Advances in Data Mining. Applications and Theoretical Aspects, 10th Industrial Conference, ICDM 2010, Berlin, Germany, July 12-14, 2010. Proceedings. LNAI 6171, pp. 101–114.
Note: This is the final draft version of this paper. Please cite this paper (or this final draft) as above.
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