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Forecasting retail sales: The case of a medium enterprise of Taiwan
Published in Artde D.K.T. Lam, Stephen D. Prior, Siu-Tsen Shen, Sheng-Joue Young, Liang-Wen Ji, Engineering Innovation and Design, 2019
Chih-Pin Freg, Yen-Ming Tseng, Chih-Cheng Huang
Market Basket Analysis is one of the key techniques used by large retailers to uncover associations between items. It works by looking for combinations of items that occur together frequently in transactions. To put it another way, it allows retailers to identify relationships between the items that people buy. Association Rules are widely used to analyze retail basket or transaction data, and are intended to identify strong rules discovered in transaction data using measures of interestingness, based on the concept of strong rules. We use market basket analysis (R. Agrawal, T. Imielinski, A. Swami, 1993) to uncover associations between items on the shelf. These association rules are widely used to analyze retail basket or transaction data, and are intended to identify strong rules discovered in transaction data using measures of interestingness, based on the concept of strong rules.
Introduction to Machine Learning
Published in Richard J. Roiger, Just Enough R!, 2020
The purpose of market basket analysis is to find interesting relationships among retail products. The results of a market basket analysis help retailers design promotions, arrange shelf or catalog items, and develop cross-marketing strategies. Association rule algorithms are often used to apply a market basket analysis to a set of data. Association rules are detailed in Chapter 7.
Using transaction data and product margins to optimise weekly flyers
Published in International Journal of Production Research, 2021
Stephen Mahar, P. Daniel Wright, Peter A. Salzarulo, Kathleen Iacocca
Market basket analysis is used to obtain πij estimates for our computational study. Market basket analysis is a commonly used data mining technique for discovering groups of items that tend to be purchased together. Given a set of customer transactions, the technique generates association rules of the form if antecedent A item(s), then consequent, B item(s). The strength of association implied by each rule is measured by confidence and lift metrics. Confidence expresses the degree of uncertainty in the association rule by comparing the number of transactions with both antecedent and consequent item(s) to the number of transactions with the antecedent item(s), or P(B|A). Lift compares the confidence of a rule to a benchmark value, where the occurrence of the consequent item(s) is independent of the occurrence of the antecedent item(s).
The Organic Reach of Online Videos: Linking Viewers’ Traits to Post-Viewing Behaviour
Published in Cybernetics and Systems, 2020
Association rules are effective for data mining techniques which are used to determine the relationships between items and variables in large databases and express such relationships in the form of rules. Data mining allows for the identification of customers’ purchasing behaviors and assists managers in formulating merchandise displays or marketing strategies (Berry and Linoff 1997). Association rules are primarily used to analyze the potential associations among goods in customers’ shopping baskets which is also known as market basket analysis.
Hit and run crash analysis using association rules mining
Published in Journal of Transportation Safety & Security, 2021
Subasish Das, Xiaoqiang Kong, Ioannis Tsapakis
The main objective of this study is to identify the crash and geometric factors that influence the decisions of drivers to flee after crashes in Louisiana. This research focuses on studying the relationship of level of severity of the crash, geometric, crash types, and environmental variables. Previous studies on this topic extensively used methods like logistic regression. To determine the most relevant patterns, this study applied ‘market basket analysis,’ which is also known as ‘association rules mining.’