K-평균 알고리즘 (Korean Wikipedia)

Analysis of information sources in references of the Wikipedia article "K-평균 알고리즘" in Korean language version.

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9doi.org

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2ams.org

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2zbmath.org

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2ucsd.edu

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2stanford.edu

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1projecteuclid.org

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1toronto.edu

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1els-cdn.com

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1arxiv.org

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1archive.today

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ams.org

Steinhaus, H. (1957). “Sur la division des corps matériels en parties”. 《Bull. Acad. Polon. Sci.》 (프랑스어) 4 (12): 801–804. MR 0090073. Zbl 0079.16403.
MacQueen, J. B. (1967). 《Some Methods for classification and Analysis of Multivariate Observations》. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press. 281–297쪽. MR 0214227. Zbl 0214.46201. 2009년 4월 7일에 확인함.

archive.org

M. R. Anderberg (1973). 《Cluster Analysis for Applications》. Academic Press.
L. Kaufman; P. J. Rousseeuw (1990). 《Finding Groups in Data. An Introduction to Cluster Analysis》. Wiley, Canada.
Kanti Mardia; 외. (1979). 《Multivariate Analysis》. Academic Press.
Manning, Christopher D.; Raghavan, Prabhakar; Schütze, Hinrich (2008). 《Introduction to Information Retrieval》. Cambridge University Press. ISBN 978-0-521-86571-5.
A. K. Jain; R. C. Dubes (1988). 《Algorithms for Clustering Data》. Prentice-Hall.
Bezdek, James C. (1981). 《Pattern Recognition with Fuzzy Objective Function Algorithms》. ISBN 0-306-40671-3.

archive.today

Cyril Goutte; Lars Kai Hansen; Matthew G. Liptrot; Egill Rostrup (2001). “Feature-Space Clustering for fMRI Meta-Analysis”. 《Human Brain Mapping》 13 (3): 165–183. doi:10.1002/hbm.1031. PMID 11376501. 2012년 12월 17일에 원본 문서에서 보존된 문서. 2015년 4월 30일에 확인함. see especially Figure 14 and appendix.

arxiv.org

Dasgupta, S.; Freund, Y. (July 2009). “Random Projection Trees for Vector Quantization”. 《Information Theory, IEEE Transactions on》 55: 3229–3242. arXiv:0805.1390. doi:10.1109/TIT.2009.2021326.

doi.org

dx.doi.org

Lloyd, S. P. (1957). “Least square quantization in PCM”. 《Bell Telephone Laboratories Paper》. Published in journal much later: Lloyd., S. P. (1982). “Least squares quantization in PCM” (PDF). 《IEEE Transactions on Information Theory》 28 (2): 129–137. doi:10.1109/TIT.1982.1056489. 2009년 4월 15일에 확인함.
Aloise, D.; Deshpande, A.; Hansen, P.; Popat, P. (2009). “NP-hardness of Euclidean sum-of-squares clustering”. 《Machine Learning》 75: 245–249. doi:10.1007/s10994-009-5103-0.
Dasgupta, S.; Freund, Y. (July 2009). “Random Projection Trees for Vector Quantization”. 《Information Theory, IEEE Transactions on》 55: 3229–3242. arXiv:0805.1390. doi:10.1109/TIT.2009.2021326.
Mahajan, M.; Nimbhorkar, P.; Varadarajan, K. (2009). “The Planar k-Means Problem is NP-Hard”. 《Lecture Notes in Computer Science》 5431: 274–285. doi:10.1007/978-3-642-00202-1_24.
Vattani., A. (2011). “k-means requires exponentially many iterations even in the plane” (PDF). 《Discrete and Computational Geometry》 45 (4): 596–616. doi:10.1007/s00454-011-9340-1.
Cyril Goutte; Lars Kai Hansen; Matthew G. Liptrot; Egill Rostrup (2001). “Feature-Space Clustering for fMRI Meta-Analysis”. 《Human Brain Mapping》 13 (3): 165–183. doi:10.1002/hbm.1031. PMID 11376501. 2012년 12월 17일에 원본 문서에서 보존된 문서. 2015년 4월 30일에 확인함. see especially Figure 14 and appendix.
Dunn, J. (1974). “Well separated clusters and optimal fuzzy partitions”. 《Journal of Cybernetics》 4: 95–104. doi:10.1080/01969727408546059.
Peter J. Rousseeuw (1987). “Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis”. 《Computational and Applied Mathematics》 20: 53–65. doi:10.1016/0377-0427(87)90125-7.
Rand, W. M. (1971). “Objective criteria for the evaluation of clustering methods”. 《Journal of the American Statistical Association》 (American Statistical Association) 66 (336): 846–850. doi:10.2307/2284239. JSTOR 2284239.

els-cdn.com

ac.els-cdn.com

J.M. Peña; J.A. Lozano; P. Larrañaga (1999). “An empirical comparison of four initialization methods for the K-Means algorithm” (PDF). ^{[깨진 링크(과거 내용 찾기)]}

jstor.org

Hartigan, J. A.; Wong, M. A. (1979). “Algorithm AS 136: A K-Means Clustering Algorithm”. 《Journal of the Royal Statistical Society, Series C》 28 (1): 100–108. JSTOR 2346830.
Rand, W. M. (1971). “Objective criteria for the evaluation of clustering methods”. 《Journal of the American Statistical Association》 (American Statistical Association) 66 (336): 846–850. doi:10.2307/2284239. JSTOR 2284239.

nih.gov

ncbi.nlm.nih.gov

Cyril Goutte; Lars Kai Hansen; Matthew G. Liptrot; Egill Rostrup (2001). “Feature-Space Clustering for fMRI Meta-Analysis”. 《Human Brain Mapping》 13 (3): 165–183. doi:10.1002/hbm.1031. PMID 11376501. 2012년 12월 17일에 원본 문서에서 보존된 문서. 2015년 4월 30일에 확인함. see especially Figure 14 and appendix.

projecteuclid.org

MacQueen, J. B. (1967). 《Some Methods for classification and Analysis of Multivariate Observations》. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press. 281–297쪽. MR 0214227. Zbl 0214.46201. 2009년 4월 7일에 확인함.

psu.edu

personal.psu.edu

Qiang Du; Tak-win Wong (2002). “Numerical Studies of MacQueen’s k-Means Algorithm for Computing the Centroidal Voronoi Tessellations” (PDF). 2015년 9월 24일에 원본 문서 (PDF)에서 보존된 문서. 2015년 4월 18일에 확인함.

stanford.edu

http://stanford.edu/~cpiech/cs221/handouts/kmeans.html

ilpubs.stanford.edu

Arthur, D.; Vassilvitskii, S. (2007). 〈k-means++: the advantages of careful seeding〉 (PDF). 《Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms》. Society for Industrial and Applied Mathematics Philadelphia, PA, USA. 1027–1035쪽. 2017년 8월 21일에 원본 문서 (PDF)에서 보존된 문서. 2015년 4월 27일에 확인함.

toronto.edu

cs.toronto.edu

Lloyd, S. P. (1957). “Least square quantization in PCM”. 《Bell Telephone Laboratories Paper》. Published in journal much later: Lloyd., S. P. (1982). “Least squares quantization in PCM” (PDF). 《IEEE Transactions on Information Theory》 28 (2): 129–137. doi:10.1109/TIT.1982.1056489. 2009년 4월 15일에 확인함.

ucsd.edu

charlotte.ucsd.edu

Hamerly, G.; Elkan, C. (2002). 〈Alternatives to the k-means algorithm that find better clusterings〉 (PDF). 《Proceedings of the eleventh international conference on Information and knowledge management (CIKM)》. 2012년 8월 5일에 원본 문서 (PDF)에서 보존된 문서. 2015년 4월 18일에 확인함.

cseweb.ucsd.edu

Vattani., A. (2011). “k-means requires exponentially many iterations even in the plane” (PDF). 《Discrete and Computational Geometry》 45 (4): 596–616. doi:10.1007/s00454-011-9340-1.

web.archive.org

J.M. Peña; J.A. Lozano; P. Larrañaga (1999). “An empirical comparison of four initialization methods for the K-Means algorithm” (PDF). ^{[깨진 링크(과거 내용 찾기)]}
Hamerly, G.; Elkan, C. (2002). 〈Alternatives to the k-means algorithm that find better clusterings〉 (PDF). 《Proceedings of the eleventh international conference on Information and knowledge management (CIKM)》. 2012년 8월 5일에 원본 문서 (PDF)에서 보존된 문서. 2015년 4월 18일에 확인함.
Qiang Du; Tak-win Wong (2002). “Numerical Studies of MacQueen’s k-Means Algorithm for Computing the Centroidal Voronoi Tessellations” (PDF). 2015년 9월 24일에 원본 문서 (PDF)에서 보존된 문서. 2015년 4월 18일에 확인함.
Arthur, D.; Vassilvitskii, S. (2007). 〈k-means++: the advantages of careful seeding〉 (PDF). 《Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms》. Society for Industrial and Applied Mathematics Philadelphia, PA, USA. 1027–1035쪽. 2017년 8월 21일에 원본 문서 (PDF)에서 보존된 문서. 2015년 4월 27일에 확인함.

wiley.com

www3.interscience.wiley.com

Cyril Goutte; Lars Kai Hansen; Matthew G. Liptrot; Egill Rostrup (2001). “Feature-Space Clustering for fMRI Meta-Analysis”. 《Human Brain Mapping》 13 (3): 165–183. doi:10.1002/hbm.1031. PMID 11376501. 2012년 12월 17일에 원본 문서에서 보존된 문서. 2015년 4월 30일에 확인함. see especially Figure 14 and appendix.

zbmath.org

Steinhaus, H. (1957). “Sur la division des corps matériels en parties”. 《Bull. Acad. Polon. Sci.》 (프랑스어) 4 (12): 801–804. MR 0090073. Zbl 0079.16403.
MacQueen, J. B. (1967). 《Some Methods for classification and Analysis of Multivariate Observations》. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press. 281–297쪽. MR 0214227. Zbl 0214.46201. 2009년 4월 7일에 확인함.