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Volumn , Issue , 2009, Pages 83-92

Heidi matrix: Nearest neighbor driven high dimensional data visualization

Author keywords

[No Author keywords available]

Indexed keywords

DATA SETS; DIMENSIONAL SPACES; HIGH DIMENSIONAL DATA; HIGH DIMENSIONAL DATA VISUALIZATION; LARGE HIGH-DIMENSIONAL DATA; MATRIX; NEAREST NEIGHBORS;

EID: 70350647263     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1562849.1562859     Document Type: Conference Paper
Times cited : (12)

References (10)
  • 1
    • 84949479246 scopus 로고    scopus 로고
    • On the surprising behavior of distance metrics in high dimensional space
    • pages 420-434
    • C. C. Aggarwal, A. Hinneburg, and D. A. Keim. On the surprising behavior of distance metrics in high dimensional space. In Lecture Notes in Computer Science, pages 420-434, 2001.
    • (2001) Lecture Notes in Computer Science
    • Aggarwal, C.C.1    Hinneburg, A.2    Keim, D.A.3
  • 4
    • 0002515778 scopus 로고    scopus 로고
    • Auditory morse analysis of triangulated manifolds
    • pages 223-236. Springer-Verlag
    • U. Axen and H. Edelsbrunner. Auditory morse analysis of triangulated manifolds. In Mathematical Visualization, pages 223-236. Springer-Verlag, 1998.
    • (1998) Mathematical Visualization
    • Axen, U.1    Edelsbrunner, H.2
  • 7
    • 85170282443 scopus 로고    scopus 로고
    • A density-based algorithm for discovering clusters in large spatial databases with noise
    • pages 226-231
    • M. Ester, H. P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. ACM SIGMOD, pages 226-231, 1996.
    • (1996) Proc. ACM SIGMOD
    • Ester, M.1    Kriegel, H.P.2    Sander, J.3    Xu, X.4
  • 8
    • 33750297146 scopus 로고    scopus 로고
    • Density-connected subspace clustering for high-dimensional data
    • K. Kailing, H. P. Kriegel, and P. Kroger. Density-connected subspace clustering for high-dimensional data. In Proc. ICDM, 2004.
    • (2004) Proc. ICDM
    • Kailing, K.1    Kriegel, H.P.2    Kroger, P.3
  • 9
    • 84878048884 scopus 로고    scopus 로고
    • A simple yet effective data clustering algorithm
    • pages 1108-1112
    • S. Vadapalli, S. Valluri, and K. Karlapalem. A simple yet effective data clustering algorithm. In Proc. ICDM, pages 1108-1112, 2006.
    • (2006) Proc. ICDM
    • Vadapalli, S.1    Valluri, S.2    Karlapalem, K.3
  • 10
    • 70350645259 scopus 로고    scopus 로고
    • Syndeca: Synthetic generation of datasets to evaluate clustering algorithms
    • J. Vennam and S. Vadapalli. Syndeca: Synthetic generation of datasets to evaluate clustering algorithms. In COMAD, 2005.
    • (2005) COMAD
    • Vennam, J.1    Vadapalli, S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.