메뉴 건너뛰기




Volumn , Issue , 2000, Pages 89-100

Local dimensionality reduction: A new approach to indexing high dimensional spaces

Author keywords

[No Author keywords available]

Indexed keywords

DIMENSIONALITY REDUCTION; EMERGING APPLICATIONS; HIGH DIMENSIONAL DATASETS; HIGH DIMENSIONAL SPACES; K-NEAREST-NEIGHBOR QUERIES; MULTI-DIMENSIONAL DATASETS; MULTI-DIMENSIONAL INDEX STRUCTURES; STATE-OF-THE-ART TECHNIQUES;

EID: 0005287692     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (195)

References (21)
  • 1
    • 0032090765 scopus 로고    scopus 로고
    • Automatic subspace clustering of high dimensional data for data mining applications
    • R. Agarwal, J. Gehrke, D. Gunopolos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. Proc. of SIGMOD. 1998.
    • (1998) Proc. of SIGMOD.
    • Agarwal, R.1    Gehrke, J.2    Gunopolos, D.3    Raghavan, P.4
  • 3
    • 0001802606 scopus 로고    scopus 로고
    • The s-tree: An index structure for high-dimensional data
    • S. Berchiold, D. A. Keim, and H. P. Knegel. The s-tree: An index structure for high-dimensional data. Proc. of VLDB, 1996.
    • (1996) Proc. of VLDB
    • Berchiold, S.1    Keim, D.A.2    Knegel, H.P.3
  • 8
    • 84976803260 scopus 로고
    • Fastmap: A fast algorithm for indexing, data-mining and visualization of traditional and multi- media datasets
    • May
    • C. Faloutsos and K.-I. D. Liii. Fastmap: A fast algorithm for indexing, data-mining and visualization of traditional and multi- media datasets. In Proc. ACM SIGMOD. pages 163-174, May 1995.
    • (1995) Proc. ACM SIGMOD. , pp. 163-174
    • Faloutsos, C.1    Liii, K.-I.D.2
  • 10
    • 0021938963 scopus 로고
    • Austeiing to minimize the maximum intercluste distance
    • T. Gonzalez. austeiing to minimize the maximum intercluste distance. Theoretical Computer Science, 1985.
    • (1985) Theoretical Computer Science
    • Gonzalez, T.1
  • 12
    • 0032091595 scopus 로고    scopus 로고
    • Cure: An efficient clustering algonthm for large databases
    • S. Guha, R. Raslogi. and K. Shim. Cure: An efficient clustering algonthm for large databases. Proc. of SIGMOD, 1998.
    • (1998) Proc. of SIGMOD
    • Guha, S.1    Raslogi, R.2    Shim, K.3
  • 13
    • 85031999247 scopus 로고
    • K-trees: A dynamic index stnacture for spatial searching
    • A. Gunman, k-trees: A dynamic index stnacture for spatial searching. In Proc. ACM SIGMOD Conf. pp. 47-57., 1984.
    • (1984) Proc. ACM SIGMOD Conf. , pp. 47-57
    • Gunman, A.1
  • 14
    • 84877318256 scopus 로고    scopus 로고
    • DimeasjonaJ redectice for similarity searching dynamic databases
    • K. V. R. Kanth, D. Agrawal. and A. K. Siagh. DimeasjonaJ redectice for similarity searching dynamic databases. Proc. of SIGMOD, 1998.
    • (1998) Proc. of SIGMOD
    • Kanth, K.V.R.1    Agrawal, D.2    Siagh, A.K.3
  • 15
    • 0031162081 scopus 로고    scopus 로고
    • The sr-tree: An index structure for high dimen,iona) nearest neighbor queries
    • N. Katayama and S. Such. The sr-tree: An index structure for high dimen,iona) nearest neighbor queries. Proc. of SJGMOD, '997.
    • (1997) Proc. of SJGMOD
    • Katayama, N.1    Such, S.2
  • 16
    • 84877347739 scopus 로고
    • The TV-tree - an index slucture for high dimensional data
    • K. Liii, H. V. Jagadish, and C. Faloutsos. The TV-tree - an index slucture for high dimensional data. In VU)R Journal, 1994.
    • (1994) VU R Journal
    • Liii, K.1    Jagadish, H.V.2    Faloutsos, C.3
  • 18
    • 0003136237 scopus 로고
    • Efficient and effective clustering methods for spatial data mining
    • R. Ng and J. Han. Efficient and effective clustering methods for spatial data mining. Proc. of VLDB, 1994.
    • (1994) Proc. of VLDB
    • Ng, R.1    Han, J.2
  • 20
    • 0000681228 scopus 로고    scopus 로고
    • A quantitative analysis and performance study for similarity-search methods in high dimensional spaces
    • R. Weber, H. Schek, and S. Blott. A quantitative analysis and performance study for similarity-search methods in high dimensional spaces. Proc. of VLDB. 1998.
    • (1998) Proc. of VLDB.
    • Weber, R.1    Schek, H.2    Blott, S.3
  • 21
    • 0030157145 scopus 로고    scopus 로고
    • Birch: An efficient data clustering method for very large databases
    • T. Zhang, R. Ramaknshnan, and M. Livny. Birch: An efficient data clustering method for very large databases. Proc. of SIGMOD. 1996.
    • (1996) Proc. of SIGMOD.
    • Zhang, T.1    Ramaknshnan, R.2    Livny, M.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.