Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A statistical theory for quantitative association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Principles of data mining
Discovering associations with numeric variables
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Scaling mining algorithms to large databases
Communications of the ACM - Evolving data mining into solutions for insights
A Microeconomic View of Data Mining
Data Mining and Knowledge Discovery
Applying Data Mining Techniques to a Health Insurance Information System
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Mining frequent item sets by opportunistic projection
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Modern Data Warehousing, Mining, and Visualization: Core Concepts
Modern Data Warehousing, Mining, and Visualization: Core Concepts
Building an Association Rules Framework to Improve Product Assortment Decisions
Data Mining and Knowledge Discovery
An Improved Branch-and-Cut Algorithm for the Capacitated Vehicle Routing Problem
Transportation Science
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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.