Genetic Based Hesitation Information Mining for Profitability Management

Abstract

Traditional Association Rule Mining has been extensively used to discover interesting rules or relationships between items in large databases but it has limitations that it solely deals with the items or products that are sold but avoids the items that are nearly sold. These nearly sold things carry hesitation data since customers are indecisive to shop for them. In this paper, with the help of vague set theory, we describe that item’s hesitation information is precious knowledge for the design of profitable selling strategies. This work proposed Genetic Algorithm based on evolution principles that has found its strong base in mining or maximize the rules for the items that customers mostly hesitate to purchase or has a high percentage of hesitation because of some reasons like price of an item, quality of an item, etc. Fitness function, crossover, and mutation are the main parameters involved in Genetic Algorithm which we used in our work. This work describes that if the reason of giving up the items is identified and resolved, we can easily remove this hesitation status of a customer and considering newly evolved rules as the interesting ones for boosting the sales of the item.