Mining of Retail Transactional Data for Revealing Seasonal Facts

Abstract

Retail market generated so many daily transactions throughout the day. This transactional data has been maintained by the bill information given to the consumers. This data set also maintains the facts regarding date and time for the particular transaction. In this paper, the goal is to mine the retail transaction data and find different facts regarding the particular season that are, WINTER, SUMMER and MONSOON. For finding these facts, the need of identifying frequent item sets is required. This frequent item sets find using classification and association techniques of data mining. The Weka tool is used for this analysis and applying different methods of classification and association. According to the research, season wise different facts regarding item groups and items in super store have been revealed which is helpful for predicating consumer behavior and product buying patterns in particular season.