Earticle

초록

영어

Examining data to find similar data is a major problem in data mining and information retrieval. There are abundant documents that contain information. Most of those documents are duplicates or near duplicates and they increase storage space and cost time for searching for information needed. Reduction of dimensionality and well organization of data are the ways that can be used to solve the problem of efficiency. In this paper we proposed a method based mined frequent terms from each document to reduce the data size and efficient method for clustering documents that have close similarity between them. Using our method only 36.4% of original size has been used. The similarity between documents is based on frequent terms shared. Our method performs well on running time of O(n) whereas the current methods for clustering require O(n3).

목차

Abstract
 1. Introduction
 2. Related Works
 3. Proposed Method
  3.1. Algorithm Description
  3.2. Time Complexity Comparison
 4. Experiment Results
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Gaudence Uwamahoro [ School of Information Science and Engineering, Central South University Changsha, 410083, China ]
• Zhang Zuping [ School of Information Science and Engineering, Central South University Changsha, 410083, China ]
• Ambele Robert Mtafya [ School of Information Science and Engineering, Central South University Changsha, 410083, China ]
• Jun Long [ School of Information Science and Engineering, Central South University Changsha, 410083, China ]

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