Earticle

초록

영어

In recent years, clustering data streams has been actively proposed in the field of data mining. In real-life domains, clustering methods for data streams should effectively monitor the continuous change of a data stream with respect to all the dimensions of the data stream. In this paper, a clustering method with frequency prediction of data elements is proposed. The incoming statistics of data elements in the monitoring range are maintained. For the range of elements with high density, the range is partitioned to detect the detailed boundary of clusters. To identifying the recent change of a data stream quickly, the support of elements is carefully monitored and predicted to determine partitioned ranges to become clusters. Considering the change of the data stream, a threshold is adaptively controlled by a prediction mechanism. By predicting the change of supports, the on-going change of a data stream can be reflected in real-time. The proposed method is comparatively analyzed by a series of experiments to identify its various characteristics.

목차

Abstract
 1. Introduction
 2. Monitoring Data Streams
 3. Prediction of the Grid-cell Support
 4. Experiments
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Nam Hun Park [ Dept. of Computer Science, Anyang University, 102 Samsungli, Buleunmyun, Ganghwagun, Incheon, Korea, 417-833 ]
• Kil Hong Joo [ Dept. of Computer Education, Gyeongin National University of Education, San 6-8 Seoksudong Manangu Anyangsi, Gyeonggi, Korea, 430-040 ] Corresponding author
• Su Young Han [ Dept. of Computer Science, Anyang University, 102 Samsungli, Buleunmyun, Ganghwagun, Incheon, Korea, 417-833 ]

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