Earticle

초록

영어

The most commonly adopted approach to find valuable information from trees data is to extract frequently occurring subtree patterns from them. Because mining frequent tree patterns has a wide range of applications such as xml mining, web usage mining, bioinformatics, and network multicast routing, many algorithms have been recently proposed to find the patterns. However, existing tree mining algorithms suffer from several serious pitfalls in finding frequent tree patterns from massive tree datasets. Some of the major problems are due to (1) the computationally high cost of the candidate maintenance, (2) the repetitious input dataset scans, and (3) the high memory dependency. These problems stem from that most of these algorithms are based on the well-known apriori algorithm and have used anti-monotone property for candidate generation and frequency counting in their algorithms. To solve the problems, we base a pattern-growth approach rather than the apriori approach, and choose to extract maximal frequent subtree patterns instead of frequent subtree patterns. We would present some new theorems derived from and evaluate the effectiveness of the proposed algorithm in comparison to the previous works.

목차

Abstract
 1. Introduction
  1.1. Motivation
  1.2. Related Work
 2. Key Features of SEAMSON
  2.1. Label projected database
  2.2. Table for candidates consideration
  2.3. Label list extension
 3. Experiments
 4. Conclusion
 References

저자

• Juryon Paik [ Dept. of Computer Eng. Sungkyunkwan Univ. ]
• Junghyun Nam [ Dept. of Computer Science Konkuk Univ. ]
• Ung Mo Kim [ Dept. of Computer Eng. Sungkyunkwan Univ. ]

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