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Chapter 10
This chapter, along with chapters 6 and 9, covered models that were trained
using unsupervised learning. In the next chapter, we move on to another learning
paradigm: reinforcement learning.
References
1. https://arxiv.org/abs/1404.1100
2. http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_
components.pdf
3. http://mplab.ucsd.edu/tutorials/pca.pdf
4. http://projector.tensorflow.org/
5. http://web.mit.edu/be.400/www/SVD/Singular_Value_Decomposition.
htm
6. https://www.deeplearningbook.org
7. Kanungo, Tapas, et al. An Efficient k-Means Clustering Algorithm: Analysis
and Implementation. IEEE transactions on pattern analysis and machine
intelligence 24.7 (2002): 881-892.
8. Ortega, Joaquín Pérez, et al. Research issues on K-means Algorithm: An
Experimental Trial Using Matlab. CEUR Workshop Proceedings: Semantic
Web and New Technologies.
9. A Tutorial on Clustering Algorithms, http://home.deib.polimi.it/
matteucc/Clustering/tutorial_html/kmeans.html.
10. Chen, Ke. On Coresets for k-Median and k-Means Clustering in Metric and
Euclidean Spaces and Their Applications. SIAM Journal on Computing 39.3
(2009): 923-947.
11. https://en.wikipedia.org/wiki/Determining_the_number_of_
clusters_in_a_ data_set.
12. Least Squares Quantization in PCM, Stuart P. Lloyd (1882), http://
www-evasion.imag.fr/people/Franck.Hetroy/Teaching/
ProjetsImage/2007/Bib/lloyd-1982.pdf
13. Dunn, J. C. (1973-01-01). A Fuzzy Relative of the ISODATA Process and Its Use
in Detecting Compact Well-Separated Clusters. Journal of Cybernetics. 3(3):
32–57.
14. Bezdek, James C. (1981). Pattern Recognition with Fuzzy Objective Function
Algorithms.
15. Peters, Georg, Fernando Crespo, Pawan Lingras, and Richard Weber. Soft
clustering–Fuzzy and rough approaches and their extensions and derivatives.
International Journal of Approximate Reasoning 54, no. 2 (2013): 307-322.
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