A pragmatics-oriented high utility mining for itemsets of size two for boosting business yields

Abstract

Retail market has paced with an enormous rate, sprawling its effect over the nations. The B2C companies have been putting lucrative offers and schemes to fetch the customers’ attractions in the awe of upbringing the business profits, but with the mindless notion of the same. Knowledge discovery in the field of data mining can be well harnessed to achieve the profit benefits. This article proposes the novel way for determining the items to be given on sale, with the logical clubs, thus extending the Apriori algorithm. The dissertation proposes the high-utility mining for itemsets of size two (HUM-IS2) Algorithm using the transactional logs of the superstores. The pruning strategies have been introduced to remove unnecessary formations of the clubs. The essence of the algorithm has been proved by experimenting with various datasets. © Springer Nature Singapore Pte Ltd. 2018.