Document Type

Article

Publication Date

7-23-2018

Publication Title

PLoS One

Volume

13

Issue

7

First page number:

1

Last page number:

24

Abstract

High utility itemset mining has become an important and critical operation in the Data Mining field. High utility itemset mining generates more profitable itemsets and the association among these itemsets, to make business decisions and strategies. Although, high utility is important, it is not the sole measure to decide efficient business strategies such as discount offers. It is very important to consider the pattern of itemsets based on the frequency as well as utility to predict more profitable itemsets. For example, in a supermarket or restaurant, beverages like champagne or wine might generate high utility (profit), but also sell less frequently compared to other beverages like soda or beer. In previous studies, it is observed that people who buy milk, bread, or diapers from a supermarket, also tend to buy beer or soda. But the items like milk, diapers, beer, or soda generate less utility (profit value) compared to beverages like champagne or wine. If we combine items like champagne or wine having high utility but less frequency, with the frequently sold items like milk, diaper, or beer, we can increase the utility of the transaction by providing some discount offers on champagne or wine. In this paper, we are integrating low-frequency itemsets with high-frequency itemsets, both having low or high utility, and provide different association rules for this combination of itemsets. In this way, we can generate a more accurate measure of pattern mining for various business strategies.

Disciplines

Computer Sciences | Numerical Analysis and Scientific Computing

File Format

pdf

File Size

4.187 Kb

Language

English

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Publisher Citation

Wu JM-T, Zhan J, Chobe S (2018) Mining Association rules for Low-Frequency itemsets. PLoS ONE 13(7): e0198066. https://doi.org/10.1371/journal.pone.0198066

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