메뉴 건너뛰기




Volumn , Issue , 2004, Pages 852-863

A Framework for Projected Clustering of High Dimensional Data Streams

Author keywords

[No Author keywords available]

Indexed keywords


EID: 85136074496     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1016/B978-012088469-8.50075-9     Document Type: Chapter
Times cited : (431)

References (18)
  • 1
  • 4
    • 0036211966 scopus 로고    scopus 로고
    • An Intuitive Framework for Understanding Changes in Evolving Data Streams
    • C.C. Aggarwal (2002) An Intuitive Framework for Understanding Changes in Evolving Data Streams. ICDE Conference
    • (2002) ICDE Conference
    • Aggarwal, C.C.1
  • 5
    • 1142291588 scopus 로고    scopus 로고
    • A Framework for Diagnosing Changes in Evolving Data Streams
    • C.C. Aggarwal (2003) A Framework for Diagnosing Changes in Evolving Data Streams. ACM SIGMOD Conference 575-586.
    • (2003) ACM SIGMOD Conference , pp. 575-586
    • Aggarwal, C.C.1
  • 6
    • 0032090765 scopus 로고    scopus 로고
    • Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications
    • R. Agrawal, J. Gehrke, D. Gunopulos and P. Raghavan (1998) Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. ACM SIGMOD Conference
    • (1998) ACM SIGMOD Conference
    • Agrawal, R.1    Gehrke, J.2    Gunopulos, D.3    Raghavan, P.4
  • 11
    • 0002679222 scopus 로고    scopus 로고
    • Scalability for Clustering Algorithms Revisited
    • F. Farnstrom, J. Lewis and C. Elkan (2000) Scalability for Clustering Algorithms Revisited. SIGKDD Explorations 2(1), 51-57.
    • (2000) SIGKDD Explorations , vol.2 , Issue.1 , pp. 51-57
    • Farnstrom, F.1    Lewis, J.2    Elkan, C.3
  • 12
    • 22044438328 scopus 로고    scopus 로고
    • Testing and spot-checking of data streams
    • J. Feigenbaum, et al. (2000) Testing and spot-checking of data streams. ACM SODA Conference
    • (2000) ACM SODA Conference
    • Feigenbaum, J.1
  • 14
    • 0032091595 scopus 로고    scopus 로고
    • CURE: An Efficient Clustering Algorithm for Large Databases
    • S. Guha, R. Rastogi and K. Shim (1998) CURE: An Efficient Clustering Algorithm for Large Databases. ACM SIGMOD Conference
    • (1998) ACM SIGMOD Conference
    • Guha, S.1    Rastogi, R.2    Shim, K.3
  • 16
    • 0003136237 scopus 로고
    • Efficient and Effective Clustering Methods for Spatial Data Mining
    • R. Ng and J. Han (1994) Efficient and Effective Clustering Methods for Spatial Data Mining. Very Large Data Bases Conference
    • (1994) Very Large Data Bases Conference
    • Ng, R.1    Han, J.2
  • 18
    • 0030157145 scopus 로고    scopus 로고
    • BIRCH: An Efficient Data. Clustering Method for Very Large Databases
    • T. Zhang, R. Ramakrishnan and M. Livny (1996) BIRCH: An Efficient Data. Clustering Method for Very Large Databases. ACM SIGMOD Conference
    • (1996) ACM SIGMOD Conference
    • Zhang, T.1    Ramakrishnan, R.2    Livny, M.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.