Earticle

초록

영어

In a dynamic database where the new transaction are inserted into the database, keeping patterns up-to-date and discovering new pattern are challenging problems of great practical importance. This may introduce new association rules and some existing association rules would become invalid. Thus, the maintenance of association rules for dynamic database is an important problem. In this paper, false positive itemset algorithm, which is an incremental algorithm, is proposed to deal with this problem. The proposed algorithm uses maximum support count of 1-itemsets obtained from previous mining to estimate infrequent itemsets, called false positive itemsets, of an original database. False positive itemsets will capable of being frequent itemsets when new transactions are inserted into an original database. This can reduce a number of times to scan an original database. This paper also proposes a new updating and pruning algorithm that guarantee to find all frequent itemsets of an updated database efficiently. The simulation results show that the proposed algorithm has a good performance.

목차

Abstract
 1. Introduction
 2. Previous Work
 3. False positive incremental association rule discovery algorithm
  3.1. Original database Discovery
  3.2. Updating frequent and false positive itemsets
 4. Experiment
 5. Conclusions
 References

저자

• Ratchadaporn Amornchewin [ Thepsatri Rajabhat University, Lopburi, Thailand ]
• Worapoj Kreesuradej [ King Mongkut’s Institute of Technology Ladkrabang, Thailand ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605