Mining Competitors and Finding Winning Plans Using Feature Scoring and Ranking-Based CMiner++ Algorithm: Finding Top-K Competitors

Sujatha T., Wilfred Blessing N. R., Suresh Palarimath

Source Title: International Journal of Intelligent Information Technologies (IJIIT)19(1)

ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781668479063|DOI: 10.4018/IJIIT.318670

MLA

Sujatha T., et al. "Mining Competitors and Finding Winning Plans Using Feature Scoring and Ranking-Based CMiner++ Algorithm: Finding Top-K Competitors." IJIIT vol.19, no.1 2023: pp.1-11. http://doi.org/10.4018/IJIIT.318670

APA

Sujatha T., Wilfred Blessing N. R., & Palarimath, S. (2023). Mining Competitors and Finding Winning Plans Using Feature Scoring and Ranking-Based CMiner++ Algorithm: Finding Top-K Competitors. International Journal of Intelligent Information Technologies (IJIIT), 19(1), 1-11. http://doi.org/10.4018/IJIIT.318670

Chicago

Sujatha T., Wilfred Blessing N. R., and Suresh Palarimath. "Mining Competitors and Finding Winning Plans Using Feature Scoring and Ranking-Based CMiner++ Algorithm: Finding Top-K Competitors," International Journal of Intelligent Information Technologies (IJIIT) 19, no.1: 1-11. http://doi.org/10.4018/IJIIT.318670

Export Reference

Favorite Full-Issue Download

View Full Text HTML

View Full Text PDF

Abstract

For a business to succeed, it is very important to make things speaking more to clients than to rivals. It is more critical to decide on the significant feature of an item which influences its competency. In spite of the works that have been done already, a few algorithms gained efficient solution. This paper proposes the CMiner++ Algorithm to assess the competitive relationship among items in unstructured dataset and finding the Top-K competitors of a given item. 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 various data sets are improved when compared to existing algorithms. In today's busy world, automatic recommendation systems are emerging because 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.