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Volumn 25, Issue 3, 2004, Pages 353-365

Clustering of interval data based on city-block distances

Author keywords

Adaptive distances; Dynamic cluster algorithm; Interval data; L1 distance; Symbolic data analysis

Indexed keywords

ALGORITHMS; DATA RECORDING; DATABASE SYSTEMS;

EID: 0346724786     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2003.10.016     Document Type: Article
Times cited : (190)

References (26)
  • 1
    • 0030636546 scopus 로고    scopus 로고
    • Mercury in the food web: Accumulation and transfer mechanisms
    • A. Sigrel, H. Sigrel (Eds.), New York: Marcel Dekker
    • Bobou A. Ribeyre F. Mercury in the food web: Accumulation and transfer mechanisms Sigrel A. Sigrel H. Metal Ions in Biological Systems 1998 289-319 Marcel Dekker New York
    • (1998) Metal Ions in Biological Systems , pp. 289-319
    • Bobou, A.1    Ribeyre, F.2
  • 3
    • 0032154982 scopus 로고    scopus 로고
    • A monothetic clustering method
    • Chavent M. A monothetic clustering method Pattern Recognition Letters 19 1998 989-996
    • (1998) Pattern Recognition Letters , vol.19 , pp. 989-996
    • Chavent, M.1
  • 4
    • 0012393548 scopus 로고    scopus 로고
    • Dynamical clustering algorithm of interval data: Optimization of an adequacy criterion based on Hausdorff distance
    • H. H. Sokolowsky, H. H. Bock Heidelberg: Springer
    • Chavent M. Lechevallier Y. Dynamical clustering algorithm of interval data: Optimization of an adequacy criterion based on Hausdorff distance Sokolowsky Bock H.H. Classification, Clustering and Data Analysis 2002 53-59 Springer Heidelberg
    • (2002) Classification, Clustering and Data Analysis , pp. 53-59
    • Chavent, M.1    Lechevallier, Y.2
  • 5
    • 0042241641 scopus 로고
    • Proximity coefficients between Boolean symbolic objects
    • E. Diday (Ed.), Heidelberg: Springer
    • de Carvalho F.A.T. Proximity coefficients between Boolean symbolic objects Diday E., et-al. New Approaches in Classification and Data Analysis 1994 387-394 Springer Heidelberg
    • (1994) New Approaches in Classification and Data Analysis , pp. 387-394
    • de Carvalho, F.A.T.1
  • 7
    • 0043135897 scopus 로고
    • The symbolic approach in clustering and related methods of data analysis
    • H. H. Bock (Ed.), Amsterdam: North-Holland
    • Diday E. The symbolic approach in clustering and related methods of data analysis Bock H.H. Classification Methods of Data Analysis 1988 673-684 North-Holland Amsterdam
    • (1988) Classification Methods of Data Analysis , pp. 673-684
    • Diday, E.1
  • 10
    • 0019111672 scopus 로고
    • Clustering analysis
    • K. S. Fu (Ed.), Heidelberg: Springer
    • Diday E. Simon J.J. Clustering analysis Fu K.S. Digital Pattern Recognition 1976 47-94 Springer Heidelberg
    • (1976) Digital Pattern Recognition , pp. 47-94
    • Diday, E.1    Simon, J.J.2
  • 13
    • 0346760234 scopus 로고    scopus 로고
    • An iterative relocation algorithm for classifying symbolic data
    • W. Gaul (Ed.), Heidelberg: Springer
    • Gordon A.D. An iterative relocation algorithm for classifying symbolic data Gaul W., et-al. Data Analysis: Scientific Modeling and Practical Application 2000 17-23 Springer Heidelberg
    • (2000) Data Analysis: Scientific Modeling and Practical Application , pp. 17-23
    • Gordon, A.D.1
  • 16
    • 0025902445 scopus 로고
    • Symbolic clustering using a new dissimilarity measure
    • Gowda K.C. Diday E. Symbolic clustering using a new dissimilarity measure Pattern Recognition 24 6 1991 567-578
    • (1991) Pattern Recognition , vol.24 , Issue.6 , pp. 567-578
    • Gowda, K.C.1    Diday, E.2
  • 18
    • 0029359322 scopus 로고
    • Divisive clustering of symbolic objects using the concepts of both similarity and dissimilarity
    • Gowda K.C. Ravi T.R. Divisive clustering of symbolic objects using the concepts of both similarity and dissimilarity Pattern Recognition 28 8 1995 1277-1282
    • (1995) Pattern Recognition , vol.28 , Issue.8 , pp. 1277-1282
    • Gowda, K.C.1    Ravi, T.R.2
  • 19
    • 0029326165 scopus 로고
    • Agglomerative clustering of symbolic objects using the concepts of both similarity and dissimilarity
    • Gowda K.C. Ravi T.R. Agglomerative clustering of symbolic objects using the concepts of both similarity and dissimilarity Pattern Recognition Letters 16 1995 647-652
    • (1995) Pattern Recognition Letters , vol.16 , pp. 647-652
    • Gowda, K.C.1    Ravi, T.R.2
  • 21
    • 0032741727 scopus 로고    scopus 로고
    • An ISODATA clustering procedure for symbolic objects using a distributed genetic algorithm
    • Gowda K.C. Ravi T.R. An ISODATA clustering procedure for symbolic objects using a distributed genetic algorithm Pattern Recognition Letters 20 1999 659-666
    • (1999) Pattern Recognition Letters , vol.20 , pp. 659-666
    • Gowda, K.C.1    Ravi, T.R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.