Data Mining Framework for Generating Sales Decision Making Information Using Association Rules

The rapid technological development in the field of information and communication technology (ICT) has enabled the databases of super shops to be organized under a countrywide sales decision making network to develop intelligent business systems by generating enriched business policies. This paper presents a data mining framework for generating sales decision making information from sales data using association rules generated from valid user input item set with respect to the sales data under analysis. The proposed framework includes super shop’s raw database storing sales data collected through sales application systems at different Point of Sale (POS) terminals. Apriori algorithm is famous for association rule discovery from the transactional database. The proposed technique using customized association rule generation and analysis checks the input items with sales data for validation of the input items. The support and confidence of each rule are computed. Sales decision making information about input items is generated by analyzing each of the generated association rules, which can be used to improve sales decision making policy to attract customers in order to increase sales. It is hoped that this approach for generating sales decision making information by analyzing sales data using association rules is more specific decision and application oriented as the business decision makers are not usually interested to all of the items of the sales database for making a specific sales decision. Keywords—databases; data mining framework; Apriori algorithm; association rule; sales decision making information