메뉴 건너뛰기




Volumn 39, Issue 2, 2009, Pages 568-574

Learning assignment order of instances for the constrained K-means clustering algorithm

Author keywords

Constrained K means clustering algorithm (Cop Kmeans); Ensemble learning; Instance level constraints

Indexed keywords

CONSTRAINED K-MEANS CLUSTERING ALGORITHM (COP-KMEANS); DATA SETS; ENSEMBLE LEARNING; GENERALIZATION PROPERTIES; INSTANCE-LEVEL CONSTRAINTS; LEARNING METHODS; REAL DATA SETS;

EID: 64049090195     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2008.2006641     Document Type: Article
Times cited : (44)

References (24)
  • 1
    • 84893405732 scopus 로고    scopus 로고
    • Data clustering: A review
    • DD, Sep
    • A. K. Jain, M. N. Murty, and P. J. Flynn, "Data clustering: A review," ACM Comput. Surv., vol. 31. no. 3. DD. 264-323. Sep. 1999.
    • (1999) ACM Comput. Surv , vol.31 , Issue.3 , pp. 264-323
    • Jain, A.K.1    Murty, M.N.2    Flynn, P.J.3
  • 3
    • 64049106765 scopus 로고    scopus 로고
    • K. Wagstaff, C. Cardie, S. Rogers, and S. Schroedl, Constrained K-means clustering with background knowledge in Proc. Int. Conf. Mach Learn., 2001, pp. 577-584.
    • K. Wagstaff, C. Cardie, S. Rogers, and S. Schroedl, "Constrained K-means clustering with background knowledge in Proc. Int. Conf. Mach Learn., 2001, pp. 577-584.
  • 7
    • 85133386144 scopus 로고    scopus 로고
    • Distance metric learning with application to clustering with side information
    • E. P. Xing, A. Y. Ng, M. I. Jordan, and S. Russell, "Distance metric learning with application to clustering with side information," in Proc. Int. Conf. Mach. Learn., 2003, pp. 505-512.
    • (2003) Proc. Int. Conf. Mach. Learn , pp. 505-512
    • Xing, E.P.1    Ng, A.Y.2    Jordan, M.I.3    Russell, S.4
  • 8
    • 33646084850 scopus 로고    scopus 로고
    • Locally linear metric adaptation with application to semi-supervised clustering and image retrieval
    • Jul
    • H. Chang and D. Y. Yeung, "Locally linear metric adaptation with application to semi-supervised clustering and image retrieval," Pattern Recognit., vol. 39, no. 7, pp. 1253-1264, Jul. 2006.
    • (2006) Pattern Recognit , vol.39 , Issue.7 , pp. 1253-1264
    • Chang, H.1    Yeung, D.Y.2
  • 9
    • 21144437828 scopus 로고    scopus 로고
    • Intelligent clustering with instance-level constraints,
    • Ph.D. dissertation, Cornell Univ, Ithaca, NY
    • K. L. Wagstaff, "Intelligent clustering with instance-level constraints," Ph.D. dissertation, Cornell Univ., Ithaca, NY, 2002.
    • (2002)
    • Wagstaff, K.L.1
  • 10
    • 84880095768 scopus 로고    scopus 로고
    • Clustering with constraints: Feasibility issues and the K-means algorithm
    • I. Davidson and S. S. Ravi, "Clustering with constraints: Feasibility issues and the K-means algorithm," in Proc. SIAM Int. Conf. Data Mining, 2005.
    • (2005) Proc. SIAM Int. Conf. Data Mining
    • Davidson, I.1    Ravi, S.S.2
  • 11
    • 21844457672 scopus 로고    scopus 로고
    • Learning a Mahalanobis metric from equivalence constraints
    • Dec
    • A. Bar-Hillel, T. Hertz, N. Shental, and D. Weinshall, "Learning a Mahalanobis metric from equivalence constraints," J. Mach. Learn. Res., vol. 6, pp. 937-965, Dec. 2005.
    • (2005) J. Mach. Learn. Res , vol.6 , pp. 937-965
    • Bar-Hillel, A.1    Hertz, T.2    Shental, N.3    Weinshall, D.4
  • 13
    • 84898984833 scopus 로고    scopus 로고
    • Semi-supervised learning with penalized probabilistic clustering
    • Cambridge, MA: MIT Press
    • Z. Lu and T. Leen, "Semi-supervised learning with penalized probabilistic clustering," in Advances in Neural Information Processing System. Cambridge, MA: MIT Press, 2005, pp. 849-856.
    • (2005) Advances in Neural Information Processing System , pp. 849-856
    • Lu, Z.1    Leen, T.2
  • 14
    • 9444294778 scopus 로고    scopus 로고
    • From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering
    • D. Klein, S. Kamvar, and C. Manning, "From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering," in Proc. Int. Conf. Mach. Learn., 2002, pp. 307-313.
    • (2002) Proc. Int. Conf. Mach. Learn , pp. 307-313
    • Klein, D.1    Kamvar, S.2    Manning, C.3
  • 16
    • 34047097838 scopus 로고    scopus 로고
    • Semi-supervised clustering: Probabilistic models, algorithms and experiments,
    • Ph.D. dissertation, Dept. Comput. Sci, Univ. Texas Austin, Austin, TX
    • S. Basu, "Semi-supervised clustering: Probabilistic models, algorithms and experiments," Ph.D. dissertation, Dept. Comput. Sci., Univ. Texas Austin, Austin, TX, 2005.
    • (2005)
    • Basu, S.1
  • 17
    • 0031209604 scopus 로고    scopus 로고
    • Selective sampling using the query by committee algorithm
    • Aug./Sep
    • Y. Freund, H. S. Seung, E. Shamir, and N. Tishby, "Selective sampling using the query by committee algorithm," Mach. Learn., vol. 28, no. 2/3, pp. 133-168, Aug./Sep. 1997.
    • (1997) Mach. Learn , vol.28 , Issue.2-3 , pp. 133-168
    • Freund, Y.1    Seung, H.S.2    Shamir, E.3    Tishby, N.4
  • 19
    • 0032139235 scopus 로고    scopus 로고
    • The random subspace method for constructing decision forests
    • Aug
    • T. K. Ho, "The random subspace method for constructing decision forests," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 8, pp. 832-844, Aug. 1998.
    • (1998) IEEE Trans. Pattern Anal. Mach. Intell , vol.20 , Issue.8 , pp. 832-844
    • Ho, T.K.1
  • 20
    • 84950632109 scopus 로고
    • Objective criteria for the evaluation of clustering methods
    • Dec
    • W. M. Rand, "Objective criteria for the evaluation of clustering methods," J. Amer. Stat. Assoc., vol. 66, no. 336, pp. 846-850, Dec. 1971.
    • (1971) J. Amer. Stat. Assoc , vol.66 , Issue.336 , pp. 846-850
    • Rand, W.M.1
  • 21
    • 44649105615 scopus 로고    scopus 로고
    • Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm
    • Sep
    • Y. Hong, S. Kwong, Y. Chang, and Q. Ren, "Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm," Pattern Recognit., vol. 41, no. 9, pp. 2747-2756, Sep. 2008.
    • (2008) Pattern Recognit , vol.41 , Issue.9 , pp. 2747-2756
    • Hong, Y.1    Kwong, S.2    Chang, Y.3    Ren, Q.4
  • 22
    • 38749139222 scopus 로고    scopus 로고
    • Consensus unsupervised feature ranking from multiple views
    • Apr
    • Y. Hong, S. Kwong, Y. Chang, and Q. Ren, "Consensus unsupervised feature ranking from multiple views," Pattern Recognit. Lett., vol. 29, no. 5, pp. 595-602, Apr. 2008.
    • (2008) Pattern Recognit. Lett , vol.29 , Issue.5 , pp. 595-602
    • Hong, Y.1    Kwong, S.2    Chang, Y.3    Ren, Q.4
  • 23
    • 43249099468 scopus 로고    scopus 로고
    • To combine steady-state genetic algorithm and ensemble learning for data clustering
    • Jul
    • Y. Hong and S. Kwong, "To combine steady-state genetic algorithm and ensemble learning for data clustering," Pattern Recognit. Lett., vol. 29, no. 9, pp. 1416-1423, Jul. 2008.
    • (2008) Pattern Recognit. Lett , vol.29 , Issue.9 , pp. 1416-1423
    • Hong, Y.1    Kwong, S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.