Big Data Mining Method of New Retail Economy Based on Association Rules
- DOI
- 10.2991/978-94-6463-030-5_159How to use a DOI?
- Keywords
- Association Rules; Big Data Of New Retail Economy; Data Mining; K-Means Clustering; Interest Degree
- Abstract
There are many sparse items in the data of new retail industry, and the extracted association rules are redundant, which leads to the problems of low temporal and spatial efficiency and poor mining quality when applied to the data mining of new retail economy. In order to improve the quality of data mining, a new retail economy big data mining method based on association rules is proposed. The k-means algorithm is used to subdivide the customer groups under the new retail economic model and extract the corresponding data association rules. Introduce interest threshold to filter association rules. The weight of frequent set is introduced to establish frequent item tree, and FP-Growth algorithm is improved to realize big data mining of new retail economy. Simulation results show that when the proposed data mining method is applied to the big data processing of the new retail economy, the execution efficiency and execution quality are improved by at least 27.5%.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Ying Liu PY - 2022 DA - 2022/12/20 TI - Big Data Mining Method of New Retail Economy Based on Association Rules BT - Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022) PB - Atlantis Press SP - 1583 EP - 1590 SN - 2589-4919 UR - https://doi.org/10.2991/978-94-6463-030-5_159 DO - 10.2991/978-94-6463-030-5_159 ID - Liu2022 ER -