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Volumn 32, Issue 10, 2011, Pages 1456-1467

Bagging-based spectral clustering ensemble selection

Author keywords

Adjusted rand index (ARI); Bagging; Normalized mutual information (NMI); Selective clustering ensembles; Spectral clustering

Indexed keywords

ADJUSTED RAND INDEX; BAGGING; NORMALIZED MUTUAL INFORMATION (NMI); SELECTIVE CLUSTERING ENSEMBLES; SPECTRAL CLUSTERING;

EID: 79956344429     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2011.04.008     Document Type: Article
Times cited : (93)

References (43)
  • 3
    • 84898936783 scopus 로고    scopus 로고
    • Blind one-microphone speech separation: A spectral learning approach
    • F.R. Bach, and M.I. Jordan Blind one-microphone speech separation: A spectral learning approach Adv. Neural Inf. Process. Systems 17 2005 65 72
    • (2005) Adv. Neural Inf. Process. Systems , vol.17 , pp. 65-72
    • Bach, F.R.1    Jordan, M.I.2
  • 4
    • 10444241978 scopus 로고    scopus 로고
    • Ensemble diversity measures and their application to thinning
    • R.E. Banfield, L.O. Hall, K.W. Bowyer, and W.P. Kegelmeyer Ensemble diversity measures and their application to thinning Inf. Fusion 6 1 2005 49 62
    • (2005) Inf. Fusion , vol.6 , Issue.1 , pp. 49-62
    • Banfield, R.E.1    Hall, L.O.2    Bowyer, K.W.3    Kegelmeyer, W.P.4
  • 6
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • L. Breiman Bagging predictors Machine Learn. 24 2 1996 123 140 (Pubitemid 126724382)
    • (1996) Machine Learning , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 7
    • 0031361611 scopus 로고    scopus 로고
    • Machine-learning research
    • T.G. Dietterich Machine-learning research AI Mag. 18 4 1997 97 136
    • (1997) AI Mag. , vol.18 , Issue.4 , pp. 97-136
    • Dietterich, T.G.1
  • 10
    • 0038391443 scopus 로고    scopus 로고
    • Bagging to improve the accuracy of a clustering procedure
    • S. Dudoit, and J. Fridlyand Bagging to improve the accuracy of a clustering procedure Bioinformatics 19 9 2003 1090
    • (2003) Bioinformatics , vol.19 , Issue.9 , pp. 1090
    • Dudoit, S.1    Fridlyand, J.2
  • 11
    • 1942517297 scopus 로고    scopus 로고
    • Random projection for high dimensional data clustering: A cluster ensemble approach
    • Fern, X.Z., Brodley, C.E., 2003. Random projection for high dimensional data clustering: A cluster ensemble approach. In: Proc. 20th Internat. Conf. Machine Learning, vol. 20, pp. 186-191.
    • (2003) Proc. 20th Internat. Conf. Machine Learning , vol.20 , pp. 186-191
    • Fern, X.Z.1    Brodley, C.E.2
  • 17
    • 33744920539 scopus 로고    scopus 로고
    • Moderate diversity for better cluster ensembles
    • DOI 10.1016/j.inffus.2005.01.008, PII S1566253505000217
    • S.T. Hadjitodorov, L.I. Kuncheva, and L.P. Todorova Moderate diversity for better cluster ensembles Inf. Fusion 7 3 2006 264 275 (Pubitemid 43842123)
    • (2006) Information Fusion , vol.7 , Issue.3 , pp. 264-275
    • Hadjitodorov, S.T.1    Kuncheva, L.I.2    Todorova, L.P.3
  • 19
    • 57249104712 scopus 로고    scopus 로고
    • Resampling-based selective clustering ensembles
    • Y. Hong, S. Kwong, H. Wang, and Q. Ren Resampling-based selective clustering ensembles Pattern Recognition Lett. 30 3 2009 298 305
    • (2009) Pattern Recognition Lett. , vol.30 , Issue.3 , pp. 298-305
    • Hong, Y.1    Kwong, S.2    Wang, H.3    Ren, Q.4
  • 20
    • 0000008146 scopus 로고
    • Comparing partitions
    • L. Hubert, and P. Arabie Comparing partitions J. Classification 2 1 1985 193 218
    • (1985) J. Classification , vol.2 , Issue.1 , pp. 193-218
    • Hubert, L.1    Arabie, P.2
  • 23
    • 0001457509 scopus 로고
    • Some methods for classification and analysis of multivariate observations
    • MacQueen, J.B., 1967. Some methods for classification and analysis of multivariate observations. In: Proc. 5th Berkeley Symp. Math. Statist., vol. 1, pp. 283-304.
    • (1967) Proc. 5th Berkeley Symp. Math. Statist. , vol.1 , pp. 283-304
    • MacQueen, J.B.1
  • 26
    • 12744281466 scopus 로고    scopus 로고
    • A comparison of resampling methods for clustering ensembles
    • Proceedings of the International Conference on Artificial Intelligence, IC-AI'04 and Proceedings of the International Conference on Machine Learning; Models, Technologies and Applications, MLMTA'04)
    • Minaei-Bidgoli, B., Topchy, A., Punch, W.F., 2004a. A comparison of resampling methods for clustering ensembles. In: Internat. Conf. on Machine Learning, Models, Technologies and Applications (MLMTA 2004), pp. 939-945. (Pubitemid 40155433)
    • (2004) Proceedings of the International Conference on Artificial Intelligence, IC-AI'04 , vol.2 , pp. 939-945
    • Minaei-Bidgoli, B.1    Topchy, A.2    Punch, W.F.3
  • 31
    • 61849098236 scopus 로고    scopus 로고
    • Pruning an ensemble of classifiers via reinforcement learning
    • I. Partalas, G. Tsoumakas, and I. Vlahavas Pruning an ensemble of classifiers via reinforcement learning Neurocomputing 72 7-9 2009 1900 1909
    • (2009) Neurocomputing , vol.72 , Issue.79 , pp. 1900-1909
    • Partalas, I.1    Tsoumakas, G.2    Vlahavas, I.3
  • 32
    • 0025448521 scopus 로고
    • The strength of weak learn ability
    • R.E. Schapire The strength of weak learn ability Machine Learn. 5 2 1990 197 227
    • (1990) Machine Learn. , vol.5 , Issue.2 , pp. 197-227
    • Schapire, R.E.1
  • 34
    • 0041965980 scopus 로고    scopus 로고
    • Cluster ensembles - A knowledge reuse framework for combining multiple partitions
    • A. Strehl, and J. Ghosh Cluster ensembles - A knowledge reuse framework for combining multiple partitions J. Machine Learn. Res. 3 2002 583 617
    • (2002) J. Machine Learn. Res. , vol.3 , pp. 583-617
    • Strehl, A.1    Ghosh, J.2
  • 39
    • 34548583274 scopus 로고    scopus 로고
    • A tutorial on spectral clustering
    • U. von Luxburg A tutorial on spectral clustering Statist. Comput. 17 4 2007 395 416
    • (2007) Statist. Comput. , vol.17 , Issue.4 , pp. 395-416
    • Von Luxburg, U.1
  • 40
    • 58249083744 scopus 로고    scopus 로고
    • Clustering aggregation by probability accumulation
    • X. Wang, C. Yang, and J. Zhou Clustering aggregation by probability accumulation Pattern Recognition 42 5 2009 668 675
    • (2009) Pattern Recognition , vol.42 , Issue.5 , pp. 668-675
    • Wang, X.1    Yang, C.2    Zhou, J.3
  • 41
    • 45849119092 scopus 로고    scopus 로고
    • Spectral clustering ensemble applied to sar image segmentation
    • X. Zhang, L. Jiao, F. Liu, L. Bo, and M. Gong Spectral clustering ensemble applied to sar image segmentation IEEE Trans. Geosci. Remote Sens. 46 7 2008 2126 2136
    • (2008) IEEE Trans. Geosci. Remote Sens. , vol.46 , Issue.7 , pp. 2126-2136
    • Zhang, X.1    Jiao, L.2    Liu, F.3    Bo, L.4    Gong, M.5
  • 42
    • 33644655237 scopus 로고    scopus 로고
    • Clusterer ensemble
    • DOI 10.1016/j.knosys.2005.11.003, PII S0950705105000985
    • Z.H. Zhou, and W. Tang Clusterer ensemble Knowl.-Based Systems 19 1 2006 77 83 (Pubitemid 43326158)
    • (2006) Knowledge-Based Systems , vol.19 , Issue.1 , pp. 77-83
    • Zhou, Z.-H.1    Tang, W.2
  • 43
    • 0036567392 scopus 로고    scopus 로고
    • Ensembling neural networks: Many could be better than all
    • DOI 10.1016/S0004-3702(02)00190-X, PII S000437020200190X
    • Z.H. Zhou, J. Wu, and W. Tang Ensembling neural networks: Many could be better than all Artif. Intell. 137 1-2 2002 239 263 (Pubitemid 34405220)
    • (2002) Artificial Intelligence , vol.137 , Issue.1-2 , pp. 239-263
    • Zhou, Z.-H.1    Wu, J.2    Tang, W.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.