Inventory Classification Using Multi-Level Association Rule Mining

Inventory Classification Using Multi-Level Association Rule Mining

Reshu Agarwal (G L Bajaj Institute of Technology and Management, Greater Noida, India) and Mandeep Mittal (Department of Mathematics, Amity Institute of Applied Sciences, Amity University Noida, Noida, India)
Copyright: © 2019 |Pages: 12
DOI: 10.4018/IJDSST.2019040101

Abstract

Popular data mining methods support knowledge discovery from patterns that hold in relations. For many applications, it is difficult to find strong associations among data items at low or primitive levels of abstraction. Mining association rules at multiple levels may lead to more informative and refined knowledge from data. Multi-level association rule mining is a variation of association rule mining for finding relationships between items at each level by applying different thresholds at different levels. In this study, an inventory classification policy is provided. At each level, the loss profit of frequent items is determined. The obtained loss profit is used to rank frequent items at each level with respect to their category, content and brand. This helps inventory manager to determine the most profitable item with respect to their category, content and brand. An example is illustrated to validate the results. Further, to comprehend the impact of above approach in the real scenario, experiments are conducted on the exiting dataset.
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1. Introduction

Data mining is the study of large quantities of data in order to find useful patterns in data. Association rule mining is a technique of data mining for finding association rules existing in the database that satisfy some minimum support and minimum confidence constraints (Agrawal & Srikant, 1994). Many researchers have focused their work on efficient mining of association rules in databases. A very influential association rule mining algorithm, Apriori, has been developed for rule mining in large transaction databases. An association rule found by apriori algorithm is of the form X→Y, where X, Y are sets of items. The support of an association rule is the ratio (in percent) of the records that contain X U Y to the total number of records in the database. For a given number of records, confidence is the ratio (in percent) of the number of records that contain X U Y to the number of records that contain X. Apriori algorithm finds all rules that meet the user-specified threshold support and confidence. Many other algorithms developed are derivative and/or extensions of this algorithm.

However, previous work has been focused on mining association rules at a single concept level. There are many applications in which mining is required at multiple levels (Kaya & Alhajj, 2004). The problem of mining association rules from transactional data was introduced by Han & Fu, 1995). For example, besides finding 70 percent of customers that purchase milk may also purchase bread, it is interesting to allow users to go to next level and show the association between toned milk and wheat bread (Rajkumar, Karthik, & Sivananda, 2003). The latter statement provides more specific and concrete information as compared to former. Therefore, a data mining system should provide efficient methods for mining multiple-level association rules (Agrawal, Imielinski, & Swami, 1993). This leads to discovery of more specific and concrete knowledge from data.

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