Journal of information and communication convergence engineering

  • 제6권4호
  • /
  • Pages.434-438
  • /
  • 2008
  • /
  • 2234-8255(pISSN)
  • /
  • 2234-8883(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
권호 표지

Path based K-means Clustering for RFID Data Sets

  • Yun, Hong-Won (Department of Information Technology, Silla University)
  • 발행 : 2008.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Massive data are continuously produced with a data rate of over several terabytes every day. These applications need effective clustering algorithms to achieve an overall high performance computation. In this paper, we propose ancestor as cluster center based approach to clustering, the K-means algorithm using ancestor. We modify the K-means algorithm. We present a clustering architecture and a clustering algorithm that minimize of I/Os and show a performance with excellent. In our experimental performance evaluation, we present that our algorithm can improve the I/O speed and the query processing time.

키워드

참고문헌

  1. H. Gonzalez, J. Han, X. Li, and D. Klabjan, "Warehousing and Analyzing Massive RFID Data Sets," 22nd IEEE ICDE Conference, 2006, p.1
  2. Fosso Wamba et al., "Enabling Intelligent B-to-B eCommerce Supply Chain Management using RFID and the EPC Network: a Case Study in the Retail Industry," International Journal of Networking and Virtural Organizations, 3(4), 2006, pp. 450-462 https://doi.org/10.1504/IJNVO.2006.011872
  3. E. Masciari, "RFID Data Management for Effective Objects Tracking," Proceedings of the 2007 ACM sysposium on Applied computing, 2007, pp. 457-461
  4. L. Golab and M. Tamer Ozsu, "Issues in Data Stream Management," SIGMOD Record, Vol. 32, No. 2, 2003, pp. 5-14 https://doi.org/10.1145/776985.776986
  5. A.K. Jain, M.N. Murty, and P.J. Flynn, "Data Clustering: A Review," ACM Computing Surveys, Vol. 31, No. 3, 1999, pp. 264-323 https://doi.org/10.1145/331499.331504
  6. K. Wagstaff and S. Rogers, "Constrained K-means Clustering with Background Knowledge," Proceeding of the Eighteenth ICML, 2001, pp. 577-584
  7. H. Nagesh, S. Goil, and A. Choudhary, "Adaptive Grids for Clustering Massive Data Sets," Proceeding of the 1st SIAM ICDM, 2001, pp. 1-17
  8. K. Alsabti, S. Ranka, and V. Singh, "An Efficient K-Means Clustering Algorithm," First Workshop on High-Performance Data Mining, 1988, pp. 1-6
  9. P.S. Bradley and U.M. Fayyad, "Refining Initial Points for K-Means Clustering," Microsoft Research TR, pp. 0-9
  10. S. Nittel, K.T. Leung, and A. Braverman, "Scaling Clustering Algorithm for Massive Data Sets using Data Streams," Proceedings of the 19th ICDM, 2003
  11. A.A. Diwn, S. Rane, S. Seshadri, and S. Sudarshan, "Clusterig Techniques for Minimizing External Path Length," Proceedings of the 22nd VLDB Conference, 1996, pp. 342-353
  12. F. Wang and P. Liu, "Temporal Management of RFID data," Proceeding of the VLDB05, 2005, pp.1128-1139