Earticle

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Association Rule Mining
  2.1. Apriori Algorithm
  2.2 Fuzzy Association Rule Mining
 3. Vague Sets
 4. Genetic Algorithm
  4.1. Some Functions of Genetic Operators
  4.2. Pseudo-Code of Genetic Algorithm
 5. Related Work
 6. Proposed Work with Achieved Result
 7. Conclusion
 References

키워드

저자

• Prateek Shrivastava [ Department of CSE & IT, Madhav Institute of Technology and Science, Gwalior (M.P.), India ]
• Akhilesh Tiwari [ Associate Professor, Department of CSE & IT, Madhav Institute of Technology and Science, Gwalior (M.P.), India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605