Mining of high utility itemsets of size-2 with pruning strategies and negative item values for B2C companies based on experiential marketing approach

Abstract

Summary Business to customer (B2C) marketing for the retail organisations is the most rapid adoption made by the developed countries, while it has the pitfalls in the developing nations. B2C companies have been continually putting some or the other lucrative offers and schemes on their manufactured products. There happens to be no logical demand of clubbing the sale of two products. The only need of such clubbing is the financial crisis which the company wants to overcome. Information technology can renew and make the competitive advantage for B2C companies. In this paper, a novel way for finding the itemset clubs is proposed, hence extending Apriori algorithm. The proposed methodology aims at finding the combinations of the products which can be sold together with the high levels of utility. This allows for making good profits for the company. Unlike contemporary way of items bearing positive values, negative item values have been looked into. The MHUIS-2wPS algorithm utilises the transactional experiences of the retail stores and outputs the size-2 clubs. The MHUI-NIV algorithm caters for the items with negative item values. The dissertation applies various pruning strategies for the discovery of high utility itemsets. These prunings will help remove the unnecessary formation of the low utility extensions. Later, various datasets have been used to show the essence of the algorithms.