메뉴 건너뛰기




Volumn 10, Issue , 2009, Pages 657-698

Nearest neighbor clustering: A baseline method for consistent clustering with arbitrary objective functions

Author keywords

Clustering; Consistency; Minimizing objective functions

Indexed keywords

BASELINE METHODS; CLUSTERING; CONSISTENCY; DATA SETS; DISCRETE OPTIMIZATION PROBLEMS; DISCRETE OPTIMIZATIONS; FUNCTION CLASS; K-NEAREST NEIGHBOR CLASSIFIERS; MINIMIZING OBJECTIVE FUNCTIONS; NEAREST NEIGHBOR CLUSTERING; OPTIMIZATION SCHEMES; QUALITY MEASURES; RESTRICTED FUNCTIONS;

EID: 64149110401     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (42)

References (31)
  • 1
    • 33847677259 scopus 로고    scopus 로고
    • A framework for statistical clustering with constant time approximation algorithms for k-median and k-means clustering
    • S. Ben-David. A framework for statistical clustering with constant time approximation algorithms for k-median and k-means clustering. Machine Learning, 66:243 - 257, 2007.
    • (2007) Machine Learning , vol.66 , pp. 243-257
    • Ben-David, S.1
  • 2
    • 0005098442 scopus 로고    scopus 로고
    • Empirical risk approximation: An induction principle for unsupervised learning
    • Technical report, University of Bonn
    • J. Buhmann. Empirical risk approximation: An induction principle for unsupervised learning. Technical report, University of Bonn, 1998.
    • (1998)
    • Buhmann, J.1
  • 3
    • 33846677514 scopus 로고    scopus 로고
    • Sublinear-time approximation algorithms for clustering via random sampling
    • 30(l-2):226-256
    • A. Czumaj and C. Sohler. Sublinear-time approximation algorithms for clustering via random sampling. Random Struct. Algorithms, 30(l-2):226-256, 2007.
    • (2007) Random Struct. Algorithms
    • Czumaj, A.1    Sohler, C.2
  • 5
    • 0036522404 scopus 로고    scopus 로고
    • Unsupervised learning of finite mixture models
    • M. Figueiredo and A. Jain. Unsupervised learning of finite mixture models. PAMI, 24(3):381-396, 2002.
    • (2002) PAMI , vol.24 , Issue.3 , pp. 381-396
    • Figueiredo, M.1    Jain, A.2
  • 6
    • 0032269108 scopus 로고    scopus 로고
    • How many clusters? Which clustering method? Answers via model-based cluster analysis
    • C. Fraley and A. Raftery. How many clusters? Which clustering method? Answers via model-based cluster analysis. Comput. J, 41(8):578-588, 1998.
    • (1998) Comput. J , vol.41 , Issue.8 , pp. 578-588
    • Fraley, C.1    Raftery, A.2
  • 7
    • 0016553238 scopus 로고
    • Distribution-free exponential error bound for nearest neighbor pattern classification
    • J. Fritz. Distribution-free exponential error bound for nearest neighbor pattern classification. IEEE Trans. Inf. Th., 21(5):552-557, 1975.
    • (1975) IEEE Trans. Inf. Th , vol.21 , Issue.5 , pp. 552-557
    • Fritz, J.1
  • 8
    • 5544291423 scopus 로고
    • The complexity of the generalized Lloyd - max problem (corresp.)
    • M. Garey, D. Johnson, and H. Witsenhausen. The complexity of the generalized Lloyd - max problem (corresp.). IEEE Trans. Inf. Theory, 28(2):255-256, 1982.
    • (1982) IEEE Trans. Inf. Theory , vol.28 , Issue.2 , pp. 255-256
    • Garey, M.1    Johnson, D.2    Witsenhausen, H.3
  • 11
    • 0001482122 scopus 로고
    • Consistency of single linkage for high-density clusters
    • J. Hartigan. Consistency of single linkage for high-density clusters. JASA, 76(374):388 - 394, 1981.
    • (1981) JASA , vol.76 , Issue.374 , pp. 388-394
    • Hartigan, J.1
  • 12
    • 33748888529 scopus 로고
    • Statistical theory in clustering
    • J. Hartigan. Statistical theory in clustering. Journal of Classification, 2:63 - 76, 1985.
    • (1985) Journal of Classification , vol.2 , pp. 63-76
    • Hartigan, J.1
  • 13
    • 0027928863 scopus 로고
    • Applications of weighted Voronoi diagrams and randomization to variance-based k-clustering
    • ACM Press, Stony Brook, USA
    • M. Inaba, N. Katoh, and H. Imai. Applications of weighted Voronoi diagrams and randomization to variance-based k-clustering. In Proceedings of the 10th Annual Symposium on Computational Geometry, pages 332-339. ACM Press, Stony Brook, USA, 1994.
    • (1994) Proceedings of the 10th Annual Symposium on Computational Geometry , pp. 332-339
    • Inaba, M.1    Katoh, N.2    Imai, H.3
  • 15
  • 16
    • 4243128193 scopus 로고    scopus 로고
    • On clusterings: Good, bad and spectral
    • R. Kannan, S. Vempala, and A. Vetta. On clusterings: Good, bad and spectral. Journal of the ACM, 51(3):497-515,2004.
    • (2004) Journal of the ACM , vol.51 , Issue.3 , pp. 497-515
    • Kannan, R.1    Vempala, S.2    Vetta, A.3
  • 17
    • 0001035413 scopus 로고
    • On the method of bounded differences
    • Cambridge University Press
    • C. McDiarmid. On the method of bounded differences. Surveys in Combinatorics, pages 148 - 188, 1989. Cambridge University Press.
    • (1989) Surveys in Combinatorics , vol.188 , pp. 148
    • McDiarmid, C.1
  • 20
    • 33749468596 scopus 로고    scopus 로고
    • Finding community structure in networks using the eigenvectors of matrices
    • M. Newman. Finding community structure in networks using the eigenvectors of matrices. Physical Review E, 74:036104, 2006.
    • (2006) Physical Review E , vol.74 , pp. 036104
    • Newman, M.1
  • 21
    • 0000963889 scopus 로고
    • Strong consistency of k-means clustering
    • D. Pollard. Strong consistency of k-means clustering. Annals of Statistics, 9(1):135 - 140, 1981.
    • (1981) Annals of Statistics , vol.9 , Issue.1 , pp. 135-140
    • Pollard, D.1
  • 22
    • 38049080280 scopus 로고    scopus 로고
    • Stability of k-means clustering
    • B. Schölkopf, J. Platt, and T. Hoffman, editors, MIT Press, Cambridge, MA
    • A. Rakhlin and A. Caponnetto. Stability of k-means clustering. In B. Schölkopf, J. Platt, and T. Hoffman, editors, Advances in Neural Information Processing Systems 19. MIT Press, Cambridge, MA, 2007.
    • (2007) Advances in Neural Information Processing Systems 19
    • Rakhlin, A.1    Caponnetto, A.2
  • 23
    • 0030381960 scopus 로고    scopus 로고
    • D. Spielman and S. Teng. Spectral partitioning works: planar graphs and finite element meshes. In 37th Annual Symposium on Foundations of Computer Science (Burlington, VT, 1996), pages 96 - 105. IEEE Comput. Soc. Press, Los Alamitos, CA, 1996. (See also extended technical report version.).
    • D. Spielman and S. Teng. Spectral partitioning works: planar graphs and finite element meshes. In 37th Annual Symposium on Foundations of Computer Science (Burlington, VT, 1996), pages 96 - 105. IEEE Comput. Soc. Press, Los Alamitos, CA, 1996. (See also extended technical report version.).
  • 26
    • 34548583274 scopus 로고    scopus 로고
    • A tutorial on spectral clustering
    • U. von Luxburg. A tutorial on spectral clustering. Statistics and Computing, 17(4):395 - 416, 2007.
    • (2007) Statistics and Computing , vol.17 , Issue.4 , pp. 395-416
    • von Luxburg, U.1
  • 29
    • 85162019956 scopus 로고    scopus 로고
    • Consistent minimization of clustering objective functions
    • J.C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, MIT Press, Cambridge, MA
    • U. von Luxburg, S. Bubeck, S. Jegelka, and M. Kaufmann. Consistent minimization of clustering objective functions. In J.C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems (NIPS) 21. MIT Press, Cambridge, MA, 2008.
    • (2008) Advances in Neural Information Processing Systems (NIPS) 21
    • von Luxburg, U.1    Bubeck, S.2    Jegelka, S.3    Kaufmann, M.4
  • 31
    • 0000039525 scopus 로고
    • A kth nearest neighbor clustering procedure
    • M. Wong and T. Lane. A kth nearest neighbor clustering procedure. J.R. Statist.Soc B, 45(3): 362-368,1983.
    • (1983) J.R. Statist.Soc B , vol.45 , Issue.3 , pp. 362-368
    • Wong, M.1    Lane, T.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.