Mining Competitors and Finding Winning Plans using Feature Scoring and Ranking based CMiner++ Algorithm

Abstract

For a business to succeed, it is very important to make things more speaking to clients than their rivals. There is constantly an issue in deciding the competitive relationship between two items and in distinguishing the principal competitors of items. It is more critical to decide on the significant feature of the item which influences its competency. In spite of the works have been done already, a few algorithms gained efficient solution. This paper proposes an efficient methodology to assess the competitive relationship among the items and come out with the leading Top-K competitors. The CMiner++ Algorithm is being proposed to assess the competitive relationship among the items in unstructured dataset and find out the Top-K competitors of a particular item of interest. Definitively, the nature of the outcomes and the versatility of this methodology utilizing numerous datasets from various areas are assessed and the efficiency and adaptability of this algorithm on different data sets are improved when evaluated with the existing algorithms. In today’s busy world, the automatic recommendation systems are emerging because the people are looking for the products best suited for them. So, it is very important to analyse the behaviour of people, make a review on large and large unstructured data sets and make the fully automated deep learning system to ensure the accurate outcome.