Abstract
Different techniques and methods have been established that have analysed consumers in shopping patterns and their usability and the real-world solutions required by them. Such information-mining techniques and analyses provide useful insights to organizations that can be really profitable while mining databases; though different techniques have some merit but also some disadvantages when their limitations are considered. Television dominates the entertainment landscape, with service providers always offering diverse programs to suit the tastes of different customers. The choices of viewers vary according to locality and season, and service providers usually bundle channels into pre-defined packages. Data mining techniques can be used to analyse customer preferences, thereby creating new, customized packages or groups of channels that suit individual needs. This paper explores consumer behaviour, focusing on the psychological factors that influence purchasing decisions and demonstrates how data mining methods can improve traditional approaches. Using an experiment based on association rule mining, the study derives rules for identifying trusted customers from sales data in the supermarket industry. Key Words: Consumer behaviour. Data mining, Association rules, Television, Supermarket
Published Version
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