Data analytics: Models and algorithms for intelligent data analysis

Data Analytics: Models and Algorithms for Intelligent Data Analysis

Volumn , Issue , 2012, Pages 1-137

  Runkler, Thomas ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CURRICULA; EDUCATION; ENGINEERING EDUCATION; INDUSTRIAL RESEARCH; RESEARCH AND DEVELOPMENT MANAGEMENT; STUDENTS; TEACHING;

DATA PREPROCESSING; DESIGN AND IMPLEMENTS; INTELLIGENT DATA ANALYSIS; MATHEMATICAL BASIS; MODELS AND ALGORITHMS; MUNICH , GERMANY; RESEARCH AND DEVELOPMENT PROJECTS; TECHNICAL UNIVERSITIES;

DATA VISUALIZATION;

EID: 84955724850     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-3-8348-2589-6     Document Type: Book

References (109)

1. U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining. AAAI Press, Menlo Park, 1996.
2. R. Kohavi, N. J. Rothleder, and E. Simoudis. Emerging trends in business analytics. Communications of the ACM, 45(8):345-48, 2002.
3. M. C. Lovell. Data mining. Review of Economics and Statistics, 65(1):1-11, 1983.
4. R. Mikut and M. Reischl. Data mining tools. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(5):431-443, 2011.
5. C. Shearer. The CRISP-DM model: The new blueprint for data mining. J Data Warehousing, 5(4):13-22, 2000.
6. S. Tyagi. Using data analytics for greater profits. Journal of Business Strategy, 24(3):12-14, 2003.
7. E. Anderson. The Irises of the Gaspe Peninsula. Bull. of the American Iris Society, 59:2-5, 1935.
8. J. C. Bezdek, J. M. Keller, R. Krishnapuram, L. I. Kuncheva, and N. R. Pal. Will the real iris data please stand up? IEEE Transactions on Fuzzy Systems, 7(3):368-369, 1999.
9. M. Blum, R. W. Floyd, V. Pratt, R. Rivest, and R. Tarjan. Time bounds for selection. Journal of Computer and System Sciences, 7:488-461, 1973.
10. R. A. Fisher. The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7:179-188, 1936.
11. R.W. Hamming. Error detecting and error correcting codes. The Bell System Technical Journal, 26(2):147-160, April 1950.
12. V. I. Levenshtein. Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady, 10(8):707-710, 1966.
13. S. S. Stevens. On the theory of scales of measurement. Science, 103(2684):677-680, 1946.
14. B. A. Barsky and D. P. Greenberg. Determining a set of B-spline control vertices to generate an interpolating surface. Computer Graphics and Image Processing, 14(3):203-226, November 1980.
15. S. Butterworth. On the theory of filter amplifiers. Wireless Engineer, 7:536-541, 1930.
16. A. V. Oppenheim, R. W. Schafer, and J. R Buck. Discrete-Time Signal Processing. Prentice Hall, 1999.
17. R. O. Duda and P. E. Hart. Pattern Classification and Scene Analysis. Wiley, New York, 1974.
18. D. Freedman and P. Diaconis. On the histogram as a density estimator: L2 theory. Probability Theory and Related Fields, 57(4):453-476, December 1981.
19. A. K. Jain. Fundamentals of Digital Image Processing. Prentice Hall, Englewood Cliffs, 1986.
20. K. Pearson. On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2(6):559-572, 1901.
21. T. A. Runkler. Fuzzy histograms and fuzzy chi-squared tests for independence. In IEEE International Conference on Fuzzy Systems, volume 3, pages 1361-1366, Budapest, July 2004.
22. J. W. Sammon. A nonlinear mapping for data structure analysis. IEEE Transactions on Computers, C-18(5):401-409, 1969.
23. D. W. Scott. On optimal and data-based histograms. Biometrika, 66(3):605-610, 1979.
24. H. A. Sturges. The choice of a class interval. Journal of the American Statistical Association, pages 65-66, 1926.
25. W. S. Torgerson. Theory and Methods of Scaling. Wiley, New York, 1958.
26. J. W. Tukey. Exploratory Data Analysis. Addison Wesley, Reading, 1987.
27. G. Young and A. S. Householder. Discussion of a set of points in terms of their mutual distances. Psychometrika, 3:19-22, 1938.
28. D. Freedman, R. Pisani, and R. Purves. Statistics. W.W. Norton & Company, New York, 2007.
29. K. Pearson. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 50(302):157-175, 1900.
30. T. A. Runkler. Fuzzy histograms and fuzzy chi-squared tests for independence. In IEEE International Conference on Fuzzy Systems, volume 3, pages 1361-1366, Budapest, July 2004.
31. H. A. Simon. Spurious correlation: A causal interpretation. Journal of the American Statistical Association, 49:467-479, 1954.
32. H. Akaike. A new look at the statistical model identification. IEEE Transactions on Automatic Control, AC-19:716-723, 1974.
33. P. Craven and G. Wahba. Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation. Numerical Mathematics, 31:377-403, 1979.
34. R. Hecht-Nielsen. Neurocomputing. Addison-Wesley, 1990.
35. K. Hornik, M. Stinchcombe, and H. White. Multilayer feedforward networks are universal approximators. Neural Networks, 2(5):359-366, 1989.
36. P. J. Huber. Robust Statistics. Wiley, New York, 2nd edition, 2009.
37. A. N. Kolmogorov. On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition. Doklady Akademii Nauk SSSR, 144:679-681, 1957.
38. R. Meiria and J. Zahavi. Using simulated annealing to optimize the feature selection problem in marketing applications. European Journal of Operational Research, 171(3):842-858, 2006.
39. J. Moody and C. Darken. Fast learning in networks of locally-tuned processing units. Neural Computation, 1:281-294, 1989.
40. F. Rosenblatt. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Reviews, 65:386-408, 1958.
41. P. J. Rousseeuw and A. M. Leroy. Robust Regression and Outlier Detection. Wiley, New York, 1987.
42. D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning internal representations by error backpropagation. In D. E. Rumelhart and J. L. McClelland, editors, Parallel Distributed Processing. Explorations in the Microstructure of Cognition, volume 1, pages 318-362. MIT Press, Cambridge, 1986.
43. W. Siedlecki and J. Sklansky. A note on genetic algorithms for large-scale feature selection. Pattern Recognition Letters, 10(5):335-347, 1989.
44. S. Vieira, J. M. Sousa, and T. A. Runkler. Multi-criteria ant feature selection using fuzzy classifiers. In C. A. Coello Coello, S. Dehuri, and S. Ghosh, editors, Swarm Intelligence for Multi-objective Problems in Data Mining, pages 19-36. Springer, 2009.
45. P. J. Werbos. The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting (Adaptive and Learning Systems for Signal Processing, Communications and Control Series). Wiley-Interscience, 1994.
46. G. E. P. Box, G. M. Jenkins, and G. C. Reinsel. Time Series Analysis: Forecasting and Control. Prentice Hall, 4th edition, 2008.
47. J E. Hopcroft, R. Motwani, and J. D. Ullman. Introduction to Automata Theory, Languages, and Computation. Addison Wesley, 3rd edition, 2006.
48. G. H. Mealy. A method for synthesizing sequential circuits. Bell System Technology Journal, 34:1045-1079, September 1955.
49. E. F. Moore. Gedankenexperiments on sequential machines. In W. R. Ashby, C. E. Shannon, and J. McCarthy, editors, Automata studies, pages 129-156. Princeton University Press, 1956.
50. H. G. Zimmermann and R. Neuneier. Modeling dynamical systems by recurrent neural networks. In International Conference on Data Mining, pages 557-566, Cambridge, 2000.
51. D. W. Aha. Editorial: Lazy learning. Artificial Intelligence Review (Special Issue on Lazy Learning), 11(1-5):7-10, June 1997.
52. R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison-Wesley, New York, 1999.
53. T. Bayes. An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53:370-418, 1763.
54. J. C. Bezdek and N. R. Pal. Two soft relatives of learning vector quantization. Neural Networks, 8(5):729-743, 1995.
55. J. C. Bezdek, T. R. Reichherzer, G. S. Lim, and Y. Attikiouzel. Multiple-prototype classifier design. IEEE Transactions on Systems, Man, and Cybernetics C, 28(1):67-79, 1998.
56. L. Breiman, J. H. Friedman, R. A. Olsen, and C. J. Stone. Classification and Regression Trees. Chapman & Hall, New Work, 1984.
57. F. L. Chung and T. Lee. Fuzzy learning vector quantization. In IEEE International Joint Conference on Neural Networks, volume 3, pages 2739-2743, Nagoya, October 1993.
58. J. Davis and M. Goadrich. The relationship between precision-recall and ROC curves. In International Conference on Machine Learning, pages 233-240, 2006.
59. R. O. Duda and P. E. Hart. Pattern Classification and Scene Analysis. Wiley, New York, 1974.
60. R. A. Fisher. The use of multiple measurements in taxonomic problems. Annals of Eugenics,7:179-188, 1936.
61. G. V. Kass. Significance testing in automatic interaction detection (AID). Applied Statistics, 24:178-189, 1975.
62. T. Kohonen. Learning vector quantization. Neural Networks, 1:303, 1988.
63. T. Kohonen. Improved versions of learning vector quantization. In International Joint Conference on Neural Networks, volume 1, pages 545-550, San Diego, June 1990.
64. J. Mercer. Functions of positive and negative type and their connection with the theory of integral equations. Philosophical Transactions of the Royal Society A, 209:415-446, 1909.
65. J. Neyman and E. S. Pearson. Interpretation of certain test criteria for purposes of statistical inference, part I. Joint Statistical Papers, Cambridge University Press, pages 1-66, 1967.
66. M. J. D. Powell. Radial basis functions for multi-variable interpolation: a review. In IMA Conference on Algorithms for Approximation of Functions and Data, pages 143-167, Shrivenham, 1985.
67. J. R. Quinlan. Induction on decision trees. Machine Learning, 11:81-106, 1986.
68. L. Rokach and O. Maimon. Data Mining with Decision Trees: Theory and Applications. Machine Perception and Artificial Intelligence. World Scientific Publishing Company, 2008.
69. B. Schölkopf and A. J. Smola. Learning with Kernels. MIT Press, Cambridge, 2002.
70. G. Shakhnarovich, T. Darrell, and P. Indyk. Nearest-Neighbor Methods in Learning and Vision: Theory and Practice. Neural Information Processing. MIT Press, 2006.
71. S. Theodoridis and K. Koutroumbas. Pattern Recognition. Academic Press, San Diego, 4th edition, 2008.
72. G. B. Ball and D. J. Hall. Isodata, an iterative method of multivariate analysis and pattern classification. In IFIPS Congress, 1965.
73. J. C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York, 1981.
74. J. C. Bezdek. Fuzzy models what are they, and why? IEEE Transactions on Fuzzy Systems, 1(1):1-6, 1993.
75. J. C. Bezdek, C. Coray, R. Gunderson, and J. Watson. Detection and characterization of cluster substructure, I. Linear structure: Fuzzy c-lines. SIAM Journal on AppliedMathematics, 40(2):339-357, April 1981.
76. J. C. Bezdek, C. Coray, R. Gunderson, and J. Watson. Detection and characterization of cluster substructure, II. Fuzzy c-varieties and convex combinations thereof. SIAM Journal on Applied Mathematics, 40(2):358-372, April 1981.
77. J. C. Bezdek and R. J. Hathaway. Optimization of fuzzy clustering criteria using genetic algorithms. In IEEE Conference on Evolutionary Computation, Orlando, volume 2, pages 589-594, June 1994.
78. J. C. Bezdek, J. M. Keller, R. Krishnapuram, and N. R. Pal. Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Kluwer, Norwell, 1999.
79. R. N. Davé. Fuzzy shell clustering and application to circle detection in digital images. International Journal on General Systems, 16:343-355, 1990.
80. R. N. Davé. Characterization and detection of noise in clustering. Pattern Recognition Letters, 12:657-664, 1991.
81. W. H. E. Day and H. Edelsbrunner. Efficient algorithms for agglomerative hierarchical clustering methods. Journal of Classification, 1(1):7-24, 1984.
82. H. Fang and Y. Saad. Farthest centroids divisive clustering. In International Conference on Machine Learning and Applications, pages 232-238, 2008.
83. M. Girolami. Mercer kernel-based clustering in feature space. IEEE Transactions on Neural Networks, 13:780-784, 2002.
84. E. E. Gustafson and W. C. Kessel. Fuzzy clustering with a covariance matrix. In IEEE International Conference on Decision and Control, San Diego, pages 761-766, 1979.
85. R. J. Hathaway and J. C. Bezdek. NERF c-means: Non-Euclidean relational fuzzy clustering. Pattern Recognition, 27:429-437, 1994.
86. R. J. Hathaway and J. C. Bezdek. Optimization of clustering criteria by reformulation. IEEE Transactions on Fuzzy Systems, 3(2):241-245, May 1995.
87. R. J. Hathaway, J. W. Davenport, and J. C. Bezdek. Relational duals of the c-means algorithms. Pattern Recognition, 22:205-212, 1989.
88. R. J. Hathaway, J. M. Huband, and J. C. Bezdek. Kernelized non-Euclidean relational fuzzy c-means algorithm. In IEEE International Conference on Fuzzy Systems, pages 414-419, Reno, May 2005.
89. A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs, 1988.
90. A. K. Jain, M. N. Murty, and P. J. Flynn. Data clustering: A review. ACM Computing Surveys, 31(3):264-323, 1999.
91. T. Kohonen. Automatic formation of topological maps of patterns in a self-organizing system. In E. Oja and O. Simula, editors, Scandinavian Conference on Image Analysis, pages 214-220, Helsinki, 1981.
92. T. Kohonen. Self-Organizing Maps. Springer, Berlin, 2001.
93. R. Krishnapuram, A. Joshi, O. Nasraoui, and L. Yi. Low-complexity fuzzy relational clustering algorithms for web mining. IEEE Transactions on Fuzzy Systems, 9(4):595-607, August 2001.
94. R. Krishnapuram and J. M. Keller. A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems, 1(2):98-110, May 1993.
95. T. A. Runkler. The effect of kernelization in relational fuzzy clustering. In GMA/GIWorkshop Fuzzy Systems and Computational Intelligence, Dortmund, pages 48-61, November 2006.
96. T. A. Runkler. Kernelized non-euclidean relational possibilistic c-means clustering. In IEEE Three Rivers Workshop on Soft Computing in Industrial Applications, Passau, August 2007.
97. T. A. Runkler. Relational fuzzy clustering. In J. Valente de Oliveira and W. Pedrycz, editors, Advances in Fuzzy Clustering and its Applications, chapter 2, pages 31-52. Wiley, 2007.
98. T. A. Runkler. Wasp swarm optimization of the c-means clustering model. International Journal of Intelligent Systems, 23(3):269-285, February 2008.
99. J. Sander, M. Ester, H.-P. Kriegel, and X. Xu. Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications. Data Mining and Knowledge Discovery, 2(2):169-194, 1998.
100. B. Schölkopf, A.J. Smola, and K. R. Müller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10:1299-1319, 1998.
101. P. Sneath and R. Sokal. Numerical Taxonomy. Freeman, San Francisco, 1973.
102. J. H. Ward. Hierarchical grouping to optimize an objective function. Journal of American Statistical Association, 58(301):236-244, 1963.
103. D. J. Willshaw and C. von der Malsburg. How patterned neural connections can be set up by self-organization. Proceedings of the Royal Society London, 194:431-445, 1976.
104. M. P. Windham. Cluster validity for the fuzzy c-means clustering algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-4(4):357-363, July 1982.
105. Z.-D. Wu, W.-X. Xie, and J.-P. Yu. Fuzzy c-means clustering algorithm based on kernel method. In International Conference on Computational Intelligence and Multimedia Applications, pages 49-54, Xi’an, 2003.
106. D.-Q. Zhang and S.-C. Chen. Fuzzy clustering using kernel method. In International Conference on Control and Automation, pages 123-127, 2002.
107. D.-Q. Zhang and S.-C. Chen. Clustering incomplete data using kernel-based fuzzy c-means algorithm. Neural Processing Letters, 18:155-162, 2003.
108. D.-Q. Zhang and S.-C. Chen. Kernel-based fuzzy and possibilistic c-means clustering. In International Conference on Artificial Neural Networks, pages 122-125, Istanbul, 2003.
109. R. Zhang and A.I. Rudnicky. A large scale clustering scheme for kernel k-means. In International Conference on Pattern Recognition, pages 289-292, Quebec, 2002.