Graphics Reference
In-Depth Information
References
1. Aggarwal, C., Reddy, C.: Data clustering: recent advances and applications. Chapman and
Hall/CRCDataMining and Knowledge Discovery Series. Taylor &Francis Group, Boca Raton
(2013)
2. Aggarwal, C.C., Reddy, C.K. (eds.): Data Clustering: Algorithms and Applications. CRC Press,
New York (2014)
3. Batista, G.E.A.P.A., Prati, R.C., Monard, M.C.: A study of the behavior of several methods for
balancing machine learning training data. SIGKDD Explor. Newsl.
6
(1), 20-29 (2004)
4. Bellman, R.E.: Adaptive control processes—a guided tour. Princeton University Press, Prince-
ton (1961)
5. Chatfield, C., Collins, A.J.: Introduction to Multivariate Analysis. Chapman and Hall, London
(1980)
6. DuMouchel, W., Volinsky, C., Johnson, T., Cortes, C., Pregibon, D.: Squashing flat files flatter.
In: Proceedings of the Fifth ACMSIGKDD International Conference on Knowledge Discovery
and Data Mining, KDD '99, pp. 6-15 (1999)
7. Dunteman, G.: Principal Components Analysis. SAGE Publications, Newbury Park (1989)
8. Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press Pro-
fessional, Inc., San Diego (1990)
9. Gan, G., Ma, C., Wu, J.: Data Clustering—Theory, Algorithms, and Applications. SIAM,
Philadelphia (2007)
10. Girolami, M., He, C.: Probability density estimation from optimally condensed data samples.
IEEE Trans. Pattern Anal. Mach. Intell.
25
(10), 1253-1264 (2003)
11. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers
Inc., San Francisco (2011)
12. Hart, P.E.: The condensed nearest neighbor rule. IEEE Trans. Inf. Theory
14
, 515-516 (1968)
13. Hwang, J., Lay, S., Lippman, A.: Nonparametric multivariate density estimation: a comparative
study. IEEE Trans. Signal Process.
42
, 2795-2810 (1994)
14. Jain, A., Zongker, D.: Feature selection: evaluation, application, and small sample performance.
IEEE Trans. Pattern Anal. Mach. Intell.
19
(2), 153-158 (1997)
15. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: A review. ACM Comput. Surv.
31
(3),
264-323 (1999)
16. Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recogn. Lett.
31
(8), 651-666
(2010)
17. Johnson, R.A., Wichern, D.W.: AppliedMultivariate Statistical Analysis. Prentice-Hall, Engle-
wood Cliffs (2001)
18. Kim, J.O., Mueller, C.W.: Factor Analysis: Statistical Methods and Practical Issues (Quantita-
tive Applications in the Social Sciences). Sage Publications, Inc, Beverly Hills (1978)
19. Kohonen, T.: The self organizing map. Proc. IEEE
78
(9), 1464-1480 (1990)
20. Madigan, D., Raghavan, N., DuMouchel, W., Nason, M., Posse, C., Ridgeway, G.: Likelihood-
based data squashing: a modeling approach to instance construction. Data Min. Knowl. Disc.
6
(2), 173-190 (2002)
21. Mitra, P., Murthy, C.A., Pal, S.K.: Density-based multiscale data condensation. IEEE Trans.
Pattern Anal. Mach. Intell.
24
(6), 734-747 (2002)
22. Nisbet, R., Elder, J., Miner, G.: Handbook of Statistical Analysis andDataMiningApplications.
Academic Press, Boston (2009)
23. Owen, A.: Data squashing by empirical likelihood. Data Min. Knowl. Disc.
7
, 101-113 (2003)
24. Refaat, M.: Data Preparation for Data Mining Using SAS. Morgan Kaufmann Publishers Inc.,
San Francisco (2007)
25. Roweis, S., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. Science
290
(5500), 2323-2326 (2000)
26. Tenenbaum, J.B., Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimen-
sionality reduction. Science
290
(5500), 2319-2323 (2000)
27. Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Trans. Neural Networks
16
(3),
645-678 (2005)
