Title: Research on fast mining of enterprise marketing investment databased on improved association rules

Authors: Yinghui Liu; Xiaosi Xu; Qixing Yin

Addresses: College of Economics and Management, Handan University, Handan 056001, Hebei, China ' College of Economics and Management, Handan University, Handan 056001, Hebei, China ' College of Economics and Management, Handan University, Handan 056001, Hebei, China

Abstract: Because of the problems of low mining precision and slow mining speed in traditional enterprise marketing investment data mining methods, a fast mining method for enterprise marketing investment databased on improved association rules is proposed. First, the enterprise marketing investment data is collected through the crawler framework, and then the collected data is cleaned. Then, the cleaned data features are extracted, and the correlation degree between features is calculated. Finally, according to the calculation results, all data items are used as constraints to reduce the number of frequent itemsets. A pruning strategy is designed in advance. Combined with the constraints, the Apriori algorithm of association rules is improved, and the improved algorithm is used to calculate all frequent itemsets, Obtain fast mining results of enterprise marketing investment data. The experimental results show that the proposed method is fast and accurate in data mining of enterprise marketing investment.

Keywords: improve association rules; enterprise marketing investment; Crawler framework; correlation degree; Apriori algorithm.

DOI: 10.1504/IJITM.2024.139592

International Journal of Information Technology and Management, 2024 Vol.23 No.3/4, pp.206 - 218

Received: 14 Nov 2022
Accepted: 27 Feb 2023
Published online: 04 Jul 2024 *

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