Earticle

초록

영어

Frequent itemset mining is a focused theme in data mining research and an important step in the analysis of data arising in a broad range of applications. The traditional exact model for frequent itemset requires that every item occur in each supporting transaction. However, real application data is usually subject to random noise. The reasons for noise are human error and measurement error. These reasons pose new challenges for the efficient discovery of frequent Itemset from the noisy data. Approximate frequent itemset mining is the discovery of itemset that are present not exactly but approximately in transactions.Most known approximate frequent Itemset mining algorithms work by explicitly stating the insertion penalty value and weight threshold. This paper presents a new method for generating insertion penalty value and weight threshold using support count of an item.

목차

Abstract
 1. Introduction
 2. Proposed System
 3. Approximate Frequent Itemset Mining
 4. Implementation
 5. Conclusion
 References

키워드

저자

• Veepu Uppal [ Assistant Professor, Departement of Computer Science & Engineering Manav Rachna College of Engineering, Faridabad, 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