商品
数据库事务
时间序列
计算机科学
系列(地层学)
质量(理念)
数据挖掘
数据库
计量经济学
数学
机器学习
业务
财务
哲学
认识论
生物
古生物学
作者
Hailin Li,Yenchun Jim Wu,Yewang Chen
标识
DOI:10.1016/j.cam.2019.112659
摘要
The correlation analysis of commodity sales is very important in cross-marketing. A means of undertaking dynamic-model-based time series data-mining was proposed to analyze the sales correlations among different commodities. A dynamic model comprises some distance models in different observation windows for a time series database that is transformed from a commodities transaction database. There are sales correlations in two time series at different times, and this may produce valuable rules and knowledge for those who wish to practice cross-marketing and earn greater profits. It means that observation time points denoting the time at which the sales correlation occurs constitute important information. The dynamic model that leverages the techniques inherent in time series data-mining can uncover the kinds of commodities that have similar sales trends and how those sales trends change within a particular time period, which indicates that the “right” commodities can be commended to the “right” customers at the “right” time. Moreover, some of the time periods used to pinpoint similar sales patterns can be used to retrieve much more valuable information, which can in turn be used to increase the sales of the correlated commodities and improve market share and profits. Analysis results of retail commodities datasets indicate that the proposed method takes into consideration the time factor, and can uncover interesting sales patterns by which to improve cross-marketing quality. Moreover, the algorithm can be regarded as an intelligent component of the recommendation and marketing systems so that human–computer interaction system can make intelligent decision.
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