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Volumn 28, Issue 2, 2013, Pages 789-807

Iterative factor clustering of binary data

Author keywords

Binary data; Categorical attribute quantification; Cluster analysis; Correspondence analysis

Indexed keywords


EID: 84875409827     PISSN: 09434062     EISSN: 16139658     Source Type: Journal    
DOI: 10.1007/s00180-012-0329-x     Document Type: Article
Times cited : (14)

References (33)
  • 1
    • 84875451230 scopus 로고
    • Cluster analysis in marketing research
    • Arabie P, Hubert L (1994) Cluster analysis in marketing research. IEEE Trans Autom Control 19: 716-723.
    • (1994) IEEE Trans Autom Control , vol.19 , pp. 716-723
    • Arabie, P.1    Hubert, L.2
  • 3
    • 33646944424 scopus 로고    scopus 로고
    • A method of predicting the number of clusters using Rands statistic
    • Chae SS, Dubien JL, Warde WD (2006) A method of predicting the number of clusters using Rands statistic. Comput Stat Data Anal 50: 3531-3546.
    • (2006) Comput Stat Data Anal , vol.50 , pp. 3531-3546
    • Chae, S.S.1    Dubien, J.L.2    Warde, W.D.3
  • 5
    • 0036011451 scopus 로고    scopus 로고
    • An examination of indexes for setermining the number of clusters in binary data sets
    • Dimitriadou E, Dolnicar S, Weingassel A (2002) An examination of indexes for setermining the number of clusters in binary data sets. Psychometrika 67: 137-160.
    • (2002) Psychometrika , vol.67 , pp. 137-160
    • Dimitriadou, E.1    Dolnicar, S.2    Weingassel, A.3
  • 7
    • 0037172724 scopus 로고    scopus 로고
    • A prediction-based resampling method for estimating the number of clusters in a dataset
    • Dudoit S, Fridlyand J (2002) A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biol 3: 1-21.
    • (2002) Genome Biol , vol.3 , pp. 1-21
    • Dudoit, S.1    Fridlyand, J.2
  • 8
    • 26944461753 scopus 로고    scopus 로고
    • Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data
    • In: Barbara D, Kamath C (eds
    • Ertoz L, Steinbach M, Kumar V (2003) Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data. In: Barbara D, Kamath C (eds) Proceedings of the third SIAM international conference on data mining, vol 112, pp 47-59.
    • (2003) Proceedings of the third SIAM international conference on data mining , vol.112 , pp. 47-59
    • Ertoz, L.1    Steinbach, M.2    Kumar, V.3
  • 10
    • 0032652570 scopus 로고    scopus 로고
    • ROCK: a robust clustering algorithm for categorical attribute
    • Guha S, Rastogi S, Shim K (2000) ROCK: a robust clustering algorithm for categorical attribute. Inform Syst 25: 512-521.
    • (2000) Inform Syst , vol.25 , pp. 512-521
    • Guha, S.1    Rastogi, S.2    Shim, K.3
  • 12
    • 77951195736 scopus 로고    scopus 로고
    • Simultaneous two-way clustering of multiple correspondence analysis
    • Hwang H, Dillon WR (2010) Simultaneous two-way clustering of multiple correspondence analysis. Multivar Behav Res 45: 186-208.
    • (2010) Multivar Behav Res , vol.45 , pp. 186-208
    • Hwang, H.1    Dillon, W.R.2
  • 13
    • 33744998469 scopus 로고    scopus 로고
    • An extension of multiple correspondence analysis for identifying heterogenous subgroups of respondents
    • Hwang H, Dillon WR, Takane Y (2006) An extension of multiple correspondence analysis for identifying heterogenous subgroups of respondents. Psychometrika 71: 161-171.
    • (2006) Psychometrika , vol.71 , pp. 161-171
    • Hwang, H.1    Dillon, W.R.2    Takane, Y.3
  • 14
    • 84856505051 scopus 로고    scopus 로고
    • Feature selection based on class-dependent densities for high-dimensional binary data
    • Javed K, Babri H, Saeed M (2012) Feature selection based on class-dependent densities for high-dimensional binary data. IEEE Trans Knowl Data Eng 24: 465-477.
    • (2012) IEEE Trans Knowl Data Eng , vol.24 , pp. 465-477
    • Javed, K.1    Babri, H.2    Saeed, M.3
  • 16
    • 79955468355 scopus 로고    scopus 로고
    • Multi-objective selection for collecting cluster alternatives
    • Kraus MJ, Müssel C, Palm G, Kestler HA (2011) Multi-objective selection for collecting cluster alternatives. Comput Stat 26: 341-353.
    • (2011) Comput Stat , vol.26 , pp. 341-353
    • Kraus, M.J.1    Müssel, C.2    Palm, G.3    Kestler, H.A.4
  • 17
    • 33947159574 scopus 로고    scopus 로고
    • Evaluation of stability of k-means cluster ensembles with respect to random initialization
    • Kuncheva LI, Vetrov DP (2005) Evaluation of stability of k-means cluster ensembles with respect to random initialization. IEEE Trans Pattern Anal 28: 1798-1808.
    • (2005) IEEE Trans Pattern Anal , vol.28 , pp. 1798-1808
    • Kuncheva, L.I.1    Vetrov, D.P.2
  • 18
    • 0032642966 scopus 로고    scopus 로고
    • The analysis of structured qualitative data
    • Lauro CN, Balbi S (1999) The analysis of structured qualitative data. Appl Stoch Model Data Anal 15: 1-27.
    • (1999) Appl Stoch Model Data Anal , vol.15 , pp. 1-27
    • Lauro, C.N.1    Balbi, S.2
  • 19
    • 0001588175 scopus 로고
    • L'analyse non symmétrique des correspondances
    • In: Diday E et al (eds), North Holland, Amsterdam
    • Lauro CN, D'Ambra L (1984) L'analyse non symmétrique des correspondances. In: Diday E et al (eds) Data analysis and informatics, III. North Holland, Amsterdam, pp 433-446.
    • (1984) Data analysis and informatics, III , pp. 433-446
    • Lauro, C.N.1    D'Ambra, L.2
  • 21
    • 77649099980 scopus 로고
    • An analysis of variance for categorical data
    • Light R, Margolin B (1971) An analysis of variance for categorical data. In J Am Stat Assoc 66: 534-544.
    • (1971) In J Am Stat Assoc , vol.66 , pp. 534-544
    • Light, R.1    Margolin, B.2
  • 22
    • 0001457509 scopus 로고
    • Some methods for classification and analysis of multivariate observations
    • In: Cam LML, Neyman J (eds), University of California Press
    • MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Cam LML, Neyman J (eds) Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol 1. University of California Press, pp 281-297.
    • (1967) Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol 1 , pp. 281-297
    • MacQueen, J.1
  • 23
    • 0038553872 scopus 로고    scopus 로고
    • A fast splitting procedure for classification and regression trees
    • Mola F, Siciliano R (1997) A fast splitting procedure for classification and regression trees. Stat Comput 7: 208-216.
    • (1997) Stat Comput , vol.7 , pp. 208-216
    • Mola, F.1    Siciliano, R.2
  • 24
    • 33744985692 scopus 로고    scopus 로고
    • An intelligent clustering clustering technique based on dual scaling
    • Nishisato S, Baba Y, Bozdogan H, Kanefuji K (eds), Springer, Tokyo
    • Mucha HJ (2002) An intelligent clustering clustering technique based on dual scaling. In: Nishisato S, Baba Y, Bozdogan H, Kanefuji K (eds) Measurement and multivariate analysis. Springer, Tokyo, pp 37-46.
    • (2002) Measurement and multivariate analysis , pp. 37-46
    • Mucha, H.J.1
  • 25
    • 34250115918 scopus 로고
    • An examination of procedures for determining the number of clusters in a data
    • Milligan GW, Cooper MC (1985) An examination of procedures for determining the number of clusters in a data. Psychometrika 50: 159-179.
    • (1985) Psychometrika , vol.50 , pp. 159-179
    • Milligan, G.W.1    Cooper, M.C.2
  • 26
    • 0035607770 scopus 로고    scopus 로고
    • Eleven ways to look at the Chi-squared coefficient for contingency tables
    • Mirkin B (2001) Eleven ways to look at the Chi-squared coefficient for contingency tables. Am Stat 55: 111-120.
    • (2001) Am Stat , vol.55 , pp. 111-120
    • Mirkin, B.1
  • 27
    • 84857058733 scopus 로고    scopus 로고
    • Choosing the number of clusters
    • Mirkin B (2011) Choosing the number of clusters. WIREs Data Mining Knowl Disc 1: 252-260.
    • (2011) WIREs Data Mining Knowl Disc , vol.1 , pp. 252-260
    • Mirkin, B.1
  • 28
    • 1542718388 scopus 로고    scopus 로고
    • Methods for the visualization of clustered climate data
    • Nocke T, Schumann H, Böhm U (2004) Methods for the visualization of clustered climate data. Comput Stat 19: 74-94.
    • (2004) Comput Stat , vol.19 , pp. 74-94
    • Nocke, T.1    Schumann, H.2    Böhm, U.3
  • 30
    • 84875478248 scopus 로고    scopus 로고
    • Factorial discriminant analysis and probabilistic models
    • Palumbo F, Siciliano R (1999) Factorial discriminant analysis and probabilistic models. In: Metron, LVI, pp 186-198.
    • (1999) In: Metron, LVI , pp. 186-198
    • Palumbo, F.1    Siciliano, R.2
  • 31
    • 0001543785 scopus 로고
    • Clustering n objects in k groups under optimal scaling of variables
    • van Buuren S, Heiser WJ (1989) Clustering n objects in k groups under optimal scaling of variables. Psychometrika 54: 699-706.
    • (1989) Psychometrika , vol.54 , pp. 699-706
    • van Buuren, S.1    Heiser, W.J.2
  • 32
    • 62849102881 scopus 로고    scopus 로고
    • Clustering and disjoint principal component analysis
    • Vichi M, Saporta G (2009) Clustering and disjoint principal component analysis. Comput Stat Data Anal 53: 3194-3208.
    • (2009) Comput Stat Data Anal , vol.53 , pp. 3194-3208
    • Vichi, M.1    Saporta, G.2
  • 33
    • 0035963877 scopus 로고    scopus 로고
    • Factorial k-means analysis for two way data
    • Vichi M, Kiers H (2001) Factorial k-means analysis for two way data. Comput Stat Data Anal 37: 49-64.
    • (2001) Comput Stat Data Anal , vol.37 , pp. 49-64
    • Vichi, M.1    Kiers, H.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.