메뉴 건너뛰기




Volumn 12, Issue 1, 2009, Pages 79-98

Development of assessment criteria for clustering algorithms

Author keywords

Clustering algorithms; Clustering performance measures; Data clustering; Unsupervised classification; Validity indices

Indexed keywords


EID: 58849140614     PISSN: 14337541     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10044-007-0099-1     Document Type: Article
Times cited : (30)

References (50)
  • 6
    • 0015644825 scopus 로고
    • A fuzzy relative of ISODATA process and its use in detecting compact well-separated clusters
    • JC Dunn 1973 A fuzzy relative of ISODATA process and its use in detecting compact well-separated clusters J Cybern 3 32 57
    • (1973) J Cybern , vol.3 , pp. 32-57
    • Dunn, J.C.1
  • 7
    • 84972893020 scopus 로고
    • A dendrite method for cluster analysis
    • RB Calinski J Harabasz 1974 A dendrite method for cluster analysis Commun Stat 3 1 27
    • (1974) Commun Stat , vol.3 , pp. 1-27
    • Calinski, R.B.1    Harabasz, J.2
  • 8
    • 0036937614 scopus 로고    scopus 로고
    • Performance evaluation of some clustering algorithms and validity indices
    • 12
    • U Maulik S Bandyopadhyay 2002 Performance evaluation of some clustering algorithms and validity indices IEEE Trans Pattern Anal Mach Intell 24 12 1650 1654
    • (2002) IEEE Trans Pattern Anal Mach Intell , vol.24 , pp. 1650-1654
    • Maulik, U.1    Bandyopadhyay, S.2
  • 9
    • 34250115918 scopus 로고
    • An examination of procedures for determining the number of clusters in a data set
    • 2
    • GW Milligan C Cooper 1985 An examination of procedures for determining the number of clusters in a data set Psychometrika 50 2 159 179
    • (1985) Psychometrika , vol.50 , pp. 159-179
    • Milligan, G.W.1    Cooper, C.2
  • 10
    • 0032269108 scopus 로고    scopus 로고
    • How many clusters? Which clustering method? Answers via model-based cluster analysis
    • 8
    • C Fraley AE Raftery 1998 How many clusters? Which clustering method? Answers via model-based cluster analysis Comput J 41 8 578 588
    • (1998) Comput J , vol.41 , pp. 578-588
    • Fraley, C.1    Raftery, A.E.2
  • 13
    • 4043077037 scopus 로고    scopus 로고
    • A new cluster validity measure and its application to image compression
    • C Chou M Su E Lai 2004 A new cluster validity measure and its application to image compression Pattern Anal Appl 7 205 220
    • (2004) Pattern Anal Appl , vol.7 , pp. 205-220
    • Chou, C.1    Su, M.2    Lai, E.3
  • 14
    • 0035532141 scopus 로고    scopus 로고
    • Estimating the number of clusters via the gap statistic
    • 2
    • R Tibshirani G Walther T Hastie 2001 Estimating the number of clusters via the gap statistic J R Stat Soc B 63 2 411 423
    • (2001) J R Stat Soc B , vol.63 , pp. 411-423
    • Tibshirani, R.1    Walther, G.2    Hastie, T.3
  • 16
    • 4644366312 scopus 로고    scopus 로고
    • Cluster validity by bootstrapping partitions
    • Department of Computer Science and Engineering, Michigan State University
    • Law MH, Jain AK (2003) Cluster validity by bootstrapping partitions. Technical report MSU-CSE-03-5, Department of Computer Science and Engineering, Michigan State University
    • (2003) Technical Report , vol.MSU-CSE-03-5
    • Law, M.H.1    Jain, A.K.2
  • 17
    • 2442611856 scopus 로고    scopus 로고
    • Stability-based validation of clustering solutions
    • T Lange M Braun JM Buhmann 2004 Stability-based validation of clustering solutions Neural Comput 16 1299 1323
    • (2004) Neural Comput , vol.16 , pp. 1299-1323
    • Lange, T.1    Braun, M.2    Buhmann, J.M.3
  • 18
    • 0036359730 scopus 로고    scopus 로고
    • A stability based method for discovering structure in clustered data
    • World Scientific, Singapore
    • Ben-Hur A, Elisseeff A, Guyon I (2002) A stability based method for discovering structure in clustered data. In: Pacific symposium on biocomputing. World Scientific, Singapore, pp 6-17
    • (2002) Pacific Symposium on Biocomputing , pp. 6-17
    • Ben-Hur, A.1    Elisseeff, A.2    Guyon, I.3
  • 19
    • 0035514007 scopus 로고    scopus 로고
    • Resampling method for unsupervised estimation of cluster validity
    • E Levine E Domany 2001 Resampling method for unsupervised estimation of cluster validity Neural Comput 13 2573 2593
    • (2001) Neural Comput , vol.13 , pp. 2573-2593
    • Levine, E.1    Domany, E.2
  • 20
    • 0023491749 scopus 로고
    • Bootstrap techniques in cluster analysis
    • A Jain J Morean 1987 Bootstrap techniques in cluster analysis Pattern Recognit 20 547 568
    • (1987) Pattern Recognit , vol.20 , pp. 547-568
    • Jain, A.1    Morean, J.2
  • 21
    • 0012452913 scopus 로고    scopus 로고
    • Cluster validation by prediction strength
    • Statistics Department, Stanford University, Stanford, CA
    • Tibshirani R, Walther G, Botstein D, Brown P (2001) Cluster validation by prediction strength. Technical report, Statistics Department, Stanford University, Stanford, CA
    • (2001) Technical Report
    • Tibshirani, R.1    Walther, G.2    Botstein, D.3    Brown, P.4
  • 22
    • 0037172724 scopus 로고    scopus 로고
    • Prediction-based resampling method for estimating the number of clusters in a data set
    • Dudoit S, Fridlyand JA (2002) Prediction-based resampling method for estimating the number of clusters in a data set. Genome Biol 3(7). Available online: http://genomebiology.com/2002/317/research/0036
    • (2002) Genome Biol , vol.3 , Issue.7
    • Dudoit, S.1    Fridlyand, J.A.2
  • 26
    • 84872177791 scopus 로고    scopus 로고
    • UCI Machine Learning. http://www.ics.uci.edu/~mlearn/MLRepository.html
    • UCI Machine Learning
  • 29
    • 0037963197 scopus 로고    scopus 로고
    • Path based clustering for grouping smooth curves and texture segmentation
    • B Fischer JM Buhmann 2003 Path based clustering for grouping smooth curves and texture segmentation IEEE Trans Pattern Anal Mach Intell 25 1 6
    • (2003) IEEE Trans Pattern Anal Mach Intell , vol.25 , pp. 1-6
    • Fischer, B.1    Buhmann, J.M.2
  • 31
    • 41849101656 scopus 로고    scopus 로고
    • Mixture-model cluster analysis using information theoretical criteria
    • JRS Fonseca MGMS Cardoso 2007 Mixture-model cluster analysis using information theoretical criteria Intell Data Anal 11 155 173
    • (2007) Intell Data Anal , vol.11 , pp. 155-173
    • Fonseca, J.R.S.1    Mgms, C.2
  • 32
    • 2442562177 scopus 로고    scopus 로고
    • A generalisation of model selection criteria
    • B Kverh A Leonardis 2004 A generalisation of model selection criteria Pattern Anal Appl 7 51 65
    • (2004) Pattern Anal Appl , vol.7 , pp. 51-65
    • Kverh, B.1    Leonardis, A.2
  • 33
    • 27844452957 scopus 로고    scopus 로고
    • Clustering spatial data with a hybrid em approach
    • T Hu Y Sung 2005 Clustering spatial data with a hybrid EM approach Pattern Anal Appl 8 139 148
    • (2005) Pattern Anal Appl , vol.8 , pp. 139-148
    • Hu, T.1    Sung, Y.2
  • 34
    • 33845291376 scopus 로고    scopus 로고
    • Investigation on several model selection criteria for determining the number of clusters
    • X Hu L Xu 2004 Investigation on several model selection criteria for determining the number of clusters Neural Inf Process Lett Rev 4 1 10
    • (2004) Neural Inf Process Lett Rev , vol.4 , pp. 1-10
    • Hu, X.1    Xu, L.2
  • 38
    • 0033411353 scopus 로고    scopus 로고
    • Colour image indexing using SOM for region-of-interest retrieval
    • 2
    • T Chen L-K Chen K-K MA 1999 Colour image indexing using SOM for region-of-interest retrieval Pattern Anal Appl 2 2 164 171
    • (1999) Pattern Anal Appl , vol.2 , pp. 164-171
    • Chen, T.1    Chen, L.-K.2    Ma, K.-K.3
  • 39
    • 0030232917 scopus 로고    scopus 로고
    • Self-organizing neural networks for automated machinery monitoring systems
    • 5
    • S Zhang R Ganesan GD Xistris 1996 Self-organizing neural networks for automated machinery monitoring systems Mech Syst Signal Process 10 5 517 532
    • (1996) Mech Syst Signal Process , vol.10 , pp. 517-532
    • Zhang, S.1    Ganesan, R.2    Xistris, G.D.3
  • 40
    • 0032287848 scopus 로고    scopus 로고
    • Image segmentation via adaptive k means clustering and knowledge-based morphological operations with biomedical operations
    • 12
    • GW Chen JB Luo KJ Parker 1998 Image segmentation via adaptive k means clustering and knowledge-based morphological operations with biomedical operations IEEE Trans Image Process 7 12 1673 1683
    • (1998) IEEE Trans Image Process , vol.7 , pp. 1673-1683
    • Chen, G.W.1    Luo, J.B.2    Parker, K.J.3
  • 41
    • 27844593269 scopus 로고    scopus 로고
    • Unsupervised learning of arbitrarily shaped clusters using ensembles of Gaussian models
    • H Frigui 2005 Unsupervised learning of arbitrarily shaped clusters using ensembles of Gaussian models Pattern Anal Appl 8 32 49
    • (2005) Pattern Anal Appl , vol.8 , pp. 32-49
    • Frigui, H.1
  • 42
    • 0001820920 scopus 로고    scopus 로고
    • X-means: Extending K-means with efficient estimation of the number of clusters
    • Morgan Kaufmann, San Francisco
    • Pelleg D, Moore AW (2000) X-means: extending K-means with efficient estimation of the number of clusters. In: Seventeenth international conference on machine learning. Morgan Kaufmann, San Francisco, pp 727-734
    • (2000) Seventeenth International Conference on Machine Learning , pp. 727-734
    • Pelleg, D.1    Moore, A.W.2
  • 44
    • 0002376884 scopus 로고
    • A survey of fuzzy clustering
    • MS Yang 1993 A survey of fuzzy clustering Math Comput Modell 18 1 16
    • (1993) Math Comput Modell , vol.18 , pp. 1-16
    • Yang, M.S.1
  • 45
    • 0033280561 scopus 로고    scopus 로고
    • A survey of fuzzy clustering algorithms for pattern recognition
    • 6
    • A Baraldi P Blonda 1999 A survey of fuzzy clustering algorithms for pattern recognition IEEE Trans Syst Man Cybern Part B 29 6 778 801
    • (1999) IEEE Trans Syst Man Cybern Part B , vol.29 , pp. 778-801
    • Baraldi, A.1    Blonda, P.2
  • 46
    • 0344925811 scopus 로고    scopus 로고
    • Novel fuzzy reinforcement learning vector quantization algorithm and its application in image compression
    • 5
    • W Xu AK Nandi J Zhang 2003 Novel fuzzy reinforcement learning vector quantization algorithm and its application in image compression IEEE Proc Vis Image Signal Process 150 5 292 298
    • (2003) IEEE Proc Vis Image Signal Process , vol.150 , pp. 292-298
    • Xu, W.1    Nandi, A.K.2    Zhang, J.3
  • 49
    • 58849117511 scopus 로고    scopus 로고
    • Microarray data using the self organising oscillator network
    • Vienna, Austria
    • Jack LB, Nandi AK (2004) Microarray data using the self organising oscillator network. In: Proceedings of EUSIPCO 2004, Vienna, Austria, pp 2183-2186
    • (2004) Proceedings of EUSIPCO 2004 , pp. 2183-2186
    • Jack, L.B.1    Nandi, A.K.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.