@inproceedings{cbf83aabff7c495ca5de099a58811565,
title = "A novel model for finding critical products with transaction logs",
abstract = "For the consumer market, finding valuable customers is the first priority and is assumed to assist companies in obtaining more profit. If we could discover critical products that are related with valuable customers, then it will lead to better marketing strategy to fulfill those essential customers. It will also assist companies in business development. This study selects real retail transaction data via the recency, frequency, and monetary (RFM) analysis and adopts the K-means algorithm to obtain results. Moreover, the Apriori algorithm with minimum support and skewness criteria is used to filter and find critical products. In this research, we found a novel methodology through setting the minimum support and skewness criteria and utilized the Apriori algorithm to identify 31 single critical products and 60 critical combinations (two products). This study assist companies in finding critical products and important customers, which is expected to provide an appropriate customer marketing strategy.",
keywords = "Association rules, Frequent itemsets, K-means, RFM, Skewness",
author = "Hsu, {Ping Yu} and Huang, {Chen Wan} and Huang, {Shih Hsiang} and Chen, {Pei Chi} and Cheng, {Ming Shien}",
note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG, part of Springer Nature.; 9th International Conference on Swarm Intelligence, ICSI 2018 ; Conference date: 17-06-2018 Through 22-06-2018",
year = "2018",
doi = "10.1007/978-3-319-93818-9_41",
language = "???core.languages.en_GB???",
isbn = "9783319938172",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "432--439",
editor = "Ying Tan and Qirong Tang and Yuhui Shi",
booktitle = "Advances in Swarm Intelligence - 9th International Conference, ICSI 2018, Proceedings",
}