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Optimal Inventory Classification using Data Mining Techniques

Optimal Inventory Classification using Data Mining Techniques

Reshu Agarwal, Mandeep Mittal, Sarla Pareek
Copyright: © 2016 |Pages: 20
ISBN13: 9781466698888|ISBN10: 1466698888|EISBN13: 9781466698895
DOI: 10.4018/978-1-4666-9888-8.ch012
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MLA

Agarwal, Reshu, et al. "Optimal Inventory Classification using Data Mining Techniques." Optimal Inventory Control and Management Techniques, edited by Mandeep Mittal and Nita H. Shah, IGI Global, 2016, pp. 236-255. https://doi.org/10.4018/978-1-4666-9888-8.ch012

APA

Agarwal, R., Mittal, M., & Pareek, S. (2016). Optimal Inventory Classification using Data Mining Techniques. In M. Mittal & N. Shah (Eds.), Optimal Inventory Control and Management Techniques (pp. 236-255). IGI Global. https://doi.org/10.4018/978-1-4666-9888-8.ch012

Chicago

Agarwal, Reshu, Mandeep Mittal, and Sarla Pareek. "Optimal Inventory Classification using Data Mining Techniques." In Optimal Inventory Control and Management Techniques, edited by Mandeep Mittal and Nita H. Shah, 236-255. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9888-8.ch012

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Abstract

Data mining has long been used in relationship extraction from large amount of data for a wide range of applications such as consumer behavior analysis in marketing. Data mining techniques, such as classification, association rule mining, temporal association rule mining, sequential pattern mining, decision trees, and clustering, have attracted attention of several researchers. Some research studies have also extended the usage of this concept in inventory management to determine the optimal economic order quantity. Yet, not many research studies have considered the application of the data mining approach on inventory classification to predict the most profitable items which is also a significant factor to the manager for optimal inventory control. In this chapter, three different cases for inventory classification based on loss rule is presented. An example is illustrated to validate the results.

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