Abstract
Traditional methods for discovering frequent patterns from large databases assume equal weights for all items of the database. In the real world, managerial decisions are based on economic values attached to the item sets. In this paper, we first introduce the concept of the value based frequent item packages problems. Then we provide an integer linear programming (ILP) model for value based optimization problems in the context of transaction data. The specific problem discussed in this paper is to find an optimal set of item packages (or item sets making up the whole transaction) that returns maximum profit to the organization under some limited resources. The specification of this problem allows us to solve a number of practical decision problems, by applying the existing and new ILP solution techniques. The model has been implemented and tested with real life retail data. The test results are reported in the paper.
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Achuthan, N.R., Gopalan, R.P., Rudra, A. (2006). Mining Value-Based Item Packages – An Integer Programming Approach. In: Williams, G.J., Simoff, S.J. (eds) Data Mining. Lecture Notes in Computer Science(), vol 3755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11677437_7
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DOI: https://doi.org/10.1007/11677437_7
Publisher Name: Springer, Berlin, Heidelberg
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