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Loss Profit Estimation Using Temporal Association Rule Mining

Author

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  • Reshu Agarwal

    (Apaji Institute of Mathematics and Applied Computer Technology, Banasthali University, Rajasthan, India)

  • Mandeep Mittal

    (Department of Computer Science Engineering, Amity School of Engineering and Technology, Bijwasan, New Delhi, India)

  • Sarla Pareek

    (Apaji Institute of Mathematics and Applied Computer Technology, Banasthali University, Rajasthan, India)

Abstract

Temporal association rule mining is a data mining technique in which relationships between items which satisfy certain timing constraints can be discovered. This paper presents the concept of temporal association rules in order to solve the problem of classification of inventories by including time expressions into association rules. Firstly, loss profit of frequent items is calculated by using temporal association rule mining algorithm. Then, the frequent items in particular time-periods are ranked according to descending order of loss profits. The manager can easily recognize most profitable items with the help of ranking found in the paper. An example is illustrated to validate the results.

Suggested Citation

  • Reshu Agarwal & Mandeep Mittal & Sarla Pareek, 2016. "Loss Profit Estimation Using Temporal Association Rule Mining," International Journal of Business Analytics (IJBAN), IGI Global, vol. 3(1), pages 45-57, January.
    • Handle: RePEc:igg:jban00:v:3:y:2016:i:1:p:45-57
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    Cited by:

    1. Merve Dogruel & Seniye Umit, 2021. "Prediction of Innovation Values of Countries Using Data Mining Decision Trees and a Comparative Application with Linear Regression Model," Istanbul Business Research, Istanbul University Business School, vol. 50(2), pages 465-493, November.

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