Data mining techniques

Acta Numerica

Volumn 10, Issue , 2001, Pages 313-355

  Hegland, Markus ^a  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 33646748455     PISSN: 09624929     EISSN: 14740508     Source Type: Journal    
DOI: 10.1017/S0962492901000058     Document Type: Article

References (93)

1. R. Agrawal, T. Imielinski and T. Swami (1993), Mining association rules between sets of items in large databases, in Proc. ACM-SIGMOD Conf. Management of Data, ACM Press, pp. 207-216.
2. R. Agrawal and R. Srikant (1995), Mining sequential patterns, in Proc. 11th Int. Conf. Data Engineering, IEEE CS Press, Los Alamitos, CA, pp. 3-14.
3. S. Bakin, M. Hegland and M. Osborne (1998), Can MARS be improved with B-splines?, in Computational Techniques and Applications: CTAC97 (B. J. Noye, M. D. Teubner and A. W. Gill, eds), World Scientific, pp. 75-82.
4. K. Ball (1997) 'An elementary introduction to modern convex geometry', in Flavors of Geometry (S. Levy, ed.), Cambridge University Press.
5. R. K. Beatson, G. Goodsell and M. J. D. Powell (1996), On multigrid techniques for thin plate spline interpolation in two dimensions, in The Mathematics of Numerical Analysis (Park City, UT, 1995), Vol. 32 of Lectures in Appl. Math., American Mathematical Society, Providence, RI, pp. 77-97.
6. R. K. Beatson and W. A. Light (1997), Fast evaluation of radial basis functions: Methods for two-dimensional polyharmonic splines, IMA J. Numer. Anal., 17 343-372.
7. R. K. Beatson and G. N. Newsam (1992), Fast evaluation of radial basis functions, I, Comput. Math. Appl., 24 7-19.
8. R. K. Beatson and M. J. D. Powell (1994), An iterative method for thin plate spline interpolation that employs approximations to Lagrange functions, in Numerical Analysis 1993, Vol. 303 of Pitman Res. Notes Math. Ser., Longman Sci. Tech., Harlow, pp. 17-39.
9. G. Bell and J. N. Gray (1997), The revolution yet to happen, in Beyond Calculation (P. J. Denning and R. M. Metcalfe, eds), Springer, pp. 5-32.
10. E. A. Bender (1996), Mathematical Methods in Artificial Intelligence, IEEE CS Press, Los Alamitos, CA.
11. M. J. A. Berry and G. Linoff (1997), Data Mining Techniques: For Marketing, Sales and Customer Support, Wiley.
12. A. Berson and S. J. Smith (1997), Data Warehousing, Data Mining, and OLAP, McGraw-Hill Series on Data Warehousing and Data Management, McGraw-Hill, New York.
13. H.-H. Bock and E. Diday, eds (2000), Analysis of Symbolic Data, Springer.
14. L. Bottou and Y. Bengio (1995), Convergence properties of the k-means algorithm, in Adv. in Neural Info. Proc. Systems, Vol. 7 (G. Tesauro and D. Touretzky, eds), MIT Press, Cambridge, MA, pp. 585-592.
15. P. Bradley, U. Fayyad and C. Reina (1998), Scaling clustering algorithms to large databases, in Proc. 4th Int. Conf. KDD, pp. 9-15.
16. L. Breiman, J. H. Friedman, R. A. Olshen and C. J. Stone (1984), Classification and Regression Trees, Wadsworth International Group, Blemont, CA.
17. S. Brin et al. (1997), Dynamic itemset counting and implication rules for market basket data, in Proc. ACM-SIGMOD Int. Conf. Management of Data, ACM Press, New York, pp. 255-264.
18. M. D. Buhmann (1993), New developments in the theory of radial basis function interpolation, in Multivariate Approximation: From CAGD to Wavelets (Santiago, 1992), Vol. 3 of Ser. Approx. Decompos., World Scientific, River Edge, NJ, pp. 35-75.
19. P. Cheeseman and J. Stutz (1996), Bayesian classification (Autoclass): Theory and results, in Advances in Knowledge Discovery and Data Mining (U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy, eds), AAAI/MIT Press, Cambridge, MA, pp. 153-180.
20. M.-S. Chen, J. Han and P. S. Yu (1996), Data mining: An overview from a database perspective, IEEE Trans. Knowledge and Data Engineering, 8, 866-883.
21. S. Clearwater and F. Provost (1990), Rl4: A tool for knowledge-based induction.
22. W. W. Cohen (1995), Fast effective rule induction, in Proc. 12th Int. Conf. Machine Learning, Morgan Kaufmann, pp. 115-123.
23. C. J. Date (1995), An Introduction to Database Systems, Addison-Wesley, Reading, MA.
24. L. Devroye, L. Györfi and G. Lugosi (1996), A Probabilistic Theory of Pattern Recognition, Vol. 31 of Applications of Mathematics, Springer.
25. I. S. Dhillon and D. S. Modha (2000), A data-clustering algorithm on distributed memory multiprocessors, in Large-Scale Parallel Data Mining (M. J. Zaki and C.-T. Ho, eds), Springer, pp. 245-260.
26. A. J. Dobson (1990), An Introduction to Generalized Linear Models, Chapman and Hall, London. Second edn of Introduction to Statistical Modelling.
27. P. Domingos and M. Pazzani (1997), On the optimality of the simple Bayesian classifier under zero-one loss, Machine Learning, 29, 103-130.
28. G. Dong and J. Li (1999), Efficient mining of emerging patterns: Discovering trends and differences, in Proc. 1999 Int. Conf. Knowledge Discovery and Data Mining (KDD'99), ACM Press, pp. 43-52.
29. R. O. Duda and P. E. Hart (1973), Pattern Classification and Scene Analysis, Wiley, New York.
30. N. Dyn (1989), Interpolation and Approximation by Radial and Related Functions, Vol. 1, Academic Press, pp. 211-234.
31. M. Ester, H.-P. Kriegel, J. Sander and X. Xu (1996), A density-based algorithm for discovering clusters in large spatial databases, in Proc. 1996 Int. Conf. Knowledge Discovery and Data Mining (KDD'96), AAAI Press, pp. 226-231.
32. A. C. Faul and M. J. D. Powell (1999) Proof of convergence of an iterative technique for thin plate spline interpolation in two dimensions, Adv. Comput. Math. 11, 183-192.
33. J. H. Friedman (1991), Multivariate adaptive regression splines, The Annals of Statistics, 19, 1-141.
34. J. H. Friedman (1994), Flexible metric nearest neighbor classification, Technical Report, Department of Statistics, Stanford University.
35. N. Friedman, D. Geiger and M. Goldszmidt (1997), Bayesian network classifiers, Machine Learning, 29, 131-163.
36. G. Golub, M. Heath and G. Wahba (1979), Generalized cross validation as a method for choosing a good ridge parameter, Technometrics, 21, 215-224.
37. A. D. Gordon (1981), Classification, Vol. 82 of Monographs on Statistics and Applied Probability, Chapman and Hall, London.
38. S. Guha, R. Rastogi and K. Shim (1998), CURE: An efficient clustering algorithm for large databases, in Proc. ACM-SIGMOD Int. Conf. Management of Data, ACM Press, pp. 73-84.
39. L. Hall, N. Chawla and K. Bowyer (1998), Decision tree learning on very large data sets, in Int. Conf. Systems, Man and Cybernetics, IEEE Press, pp. 2579-2584.
40. L. O. Hall, N. Chawla, K. W. Bowyer and W. P. Kegelmayer (2000), Learning rules from distributed data, in Large-Scale Parallel Data Mining (M. J. Zaki and C.-T. Ho, eds), Springer, pp. 211-220.
41. J. Han and M. Kamber (2001), Data Mining, Concepts and Techniques, Morgan Kaufmann.
42. J. A. Hartigan (1975), Clustering Algorithms, Wiley Series in Probability and Mathematical Statistics, Wiley, New York/London/Sydney.
43. T. J. Hastie and R. J. Tibshirani (1990), Generalized Additive Models, Vol. 43 of Monographs on Statistics and Applied Probability, Chapman and Hall.
44. D. Heckerman, D. Geiger and D. E. Chickering (1995), Learning Bayesian networks, Machine Learning, 20, 197-243.
45. G. H. John and P. Langley (1995), Estimating continuous distributions in Bayesian classifiers, in Proc. 11th Conference on Uncertainty in Artificial Intelligence (P. Besnard and S. Hanks, eds), Morgan Kaufmann, pp. 338-345.
46. E. L. Johnson and H. Kargupta (2000), Collective, hierarchical clustering from distributed heterogeneous data, in Large-Scale Parallel Data Mining (M. J. Zaki and C.-T. Ho, eds), Springer, pp. 221-244.
47. L. Kaufman and P. J. Rousseeuw (1990), Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York.
48. P. Langley, W. Iba and K. Thompson (1992), An analysis of Bayesian classifiers, in Proc. 10th Nat. Conf. Artificial Intelligence (W. Swartout, ed.), AAAI Press, pp. 223-228.
49. P. Langley and S. Sage (1994), Induction of selective Bayesian classifiers, in Proc. 10th Conf. Uncertainty in Artificial Intelligence (R. L. Mantaras and D. Poole, eds), Morgan Kaufmann, pp. 399-406.
50. P. Langley and S. Sage (1999), Tractable average-case analysis of naive Bayesian classifiers, in Proc. 16th Int. Conf. Machine Learning, Bled, Slovenia, Morgan Kaufmann, pp. 220-228.
51. B. Lent, A. Swami and J. Widom (1997), Clustering association rules, in Proc. 1997 Int. Conf. Data Engineering (ICDE'97), IEEE CS Press, pp. 220-231.
52. J. Li, G. Dong and K. Ramamohanrarao (2000), Making use of the most expressive jumping emerging patterns for classification, in Proc. 2000 Pacific-Asia Conf. Knowledge Discovery and Data Mining (PAKDD'00), Springer, pp. 220-232.
53. B. Liu, W. Hsu and Y. Ma (1998), Integrating classification and association rule mining, in Proc. 1998 Int. Conf. Knowledge Discovery and Data Mining (KDD'98), AAAI Press, pp. 80-86.
54. J. MacQueen (1967), Some methods for classification and analysis of multivariate observations, in Proc. 5th Berkeley Sympos. Math. Statist. and Probability (1965/66), Vol. I: Statistics, University of California Press, Berkeley, CA, pp. 281-297.
55. G. J. McLachlan and T. Krishnan (1996), The EM Algorithm and Extensions, Wiley.
56. H. Mannila, H. Toivonen and A. I. Verkamo (1995), Discovering frequent episodes in sequences, in Proc. 1st Int. Conf. Knowledge Discovery Databases and Data Mining (KDD'95), AAAI Press, pp. 210-215.
57. H. Mannila and H. Toivonen (1996), Discovering generalized episodes using minimal occurrences, in Proc. 2nd Int. Conf. Knowledge Discovery Databases and Data Mining (KDD'96), AAAI Press, pp. 146-151.
58. D. Meretakis and B. Wüthrich (1999), Extending naive Bayes classifiers using long itemsets, in Proc. 1999 Int. Conf. Knowledge Discovery and Data Mining (KDD'99), ACM Press, pp. 165-174.
59. V. D. Milman and G. Schechtman (1986) Asymptotic Theory of Finite Dimensional Normed Spaces, Vol. 1200 of Lecture Notes in Mathematics, Springer.
60. T. M. Mitchell (1997), Machine Learning, McGraw-Hill.
61. S. Morishita (1998), On classification and regression, in Proc. 1st Int. Conf. Discovery Science, Vol. 1532, Springer, pp. 40-57.
62. S. Morishita and A. Nakaya (2000), Parallel branch-and-bound graph search for correlated association rules, in Large-Scale Parallel Data Mining (M. J. Zaki and C.-T. Ho, eds), Springer, pp. 127-144.
63. R. Ng and J. Han (1994), Efficient and effective clustering methods for spatial data mining, in Proc. 20th Int. Conf. Very Large Data Bases, Morgan Kaufmann, pp. 144-155.
64. T. Oates, M. D. Schmill, D. Jensen and P. R. Cohen (1997), A family of algorithms for finding temporal structure in data, in Preliminary Papers of the 6th Int. Workshop on AI and Statistics, Society for Artificial Intelligence and Statistics, pp. 371-378.
65. C. Olson (1995), Parallel algorithms for hierarchical clustering, Parallel Computing, 8 1313-1325.
66. J. S. Park, M. S. Chen and P. S. Yu (1995a), An effective hash-based algorithm for mining association rules, in Proc. 1995 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'95), ACM Press, pp. 175-186.
67. J. S. Park, M. S. Chen and P. S. Yu (1995b), Efficient parallel data mining for association rules, in Proc. 4th Int. Conf. Information and Knowledge Management, ACM Press, pp. 31-36.
68. M. J. D. Powell (1992), The theory of radial basis function approximation in 1990, in Advances in Numerical Analysis, Vol. II (Lancaster, 1990), Oxford Sci. Publ., Oxford University Press, New York, pp. 105-210.
69. F. Provost, J. Aronis and B. Buchanan (2000), Rule-space search for knowledge-based discovery, CIIO Working Paper Number #IS 99-0012, Stern School of Business, New York University, NY. Available from: http://www.stern.nyu.edu/fprovost/
70. J. R. Quinlan (1986), Induction of decision trees, Machine Learning, 1 81-106.
71. J. R. Quinlan (1987), Generating production rules from decision trees, in Proc. 10th Int. Conf. Artificial Intelligence (IJCAI-87), Morgan Kaufmann, pp. 304-307.
72. J. R. Quinlan (1993), C4.5: Programs for Machine Learning, Morgan Kaufmann.
73. N. Ramakrishnan and A. Y. Grama (1999), Data mining: From serendipity to science, Computer, 32(8) 34-37.
74. S. Ramaswamy, S. Mahajan and A. Silbershatz (1998), On the discovery of interesting patterns in association rules, in Proc. 24th Int. Conf. Very Large Data Bases, Morgan Kaufmann, pp. 368-379.
75. B. D. Ripley (1996), Pattern Recognition and Neural Networks, Cambridge University Press, Cambridge.
76. A. Savasere, E. Omieski and S. Navanthe (1995), An efficient algorithm for mining association rules in large databases, in Proc. 21st Int. Conf. Very Large Data Bases, Morgan Kaufmann, pp. 432-444.
77. R. Segal and O. Etioni (1994), Learning decision lists using homogeneous rules, in Proc. 12th Nat. Conf. Artificial Intelligence, AAAI Press, pp. 619-625.
78. J. Shafer, R. Agrawal and M. Mehta (1996), SPRINT: A scalable parallel classifier for data mining, in Proc. 1996 Int. Conf. Very Large Data Bases (VLDB'96), Morgan Kaufmann, pp. 544-555.
79. T. Shintani and M. Kitsuregawa (2000), Parallel generalized association rule mining on large scale PC clusters, in Large-Scale Parallel Data Mining (M. J. Zaki and C.-T. Ho, eds), Springer, pp. 145-160.
80. R. Sibson (1973), SLINK: An optimally efficient algorithm for the single-link cluster method, The Computer Journal, 16 30-34.
81. A. Silberschatz and A. Tuzhilin (1996), What makes patterns interesting in knowledge discovery systems, IEEE Trans. Knowledge and Data Engineering, 8 970-974.
82. R. Srikant and R. Agrawal (1995), Mining generalized association rules, in Proc. 1995 Int. Conf. Very Large Data Bases (VLDB'95), pp. 407-419.
83. R. Srikant and R. Agrawal (1996a), Mining quantitative association rules in large relational tables, in Proc. 1996 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'96), ACM Press, pp. 1-12.
84. R. Srikant and R. Agrawal (1996b), Mining sequential patterns: Generalizations and performance improvements, in 5th Int. Conf. Extending Database Technology, Vol. 1057 of Lecture Notes in Computer Science, Springer, pp. 3-17.
85. M. Talagrand (1996), A new look at independence, Ann. Prob., 23 1-37.
86. H. Toivonen (1996), Sampling large databases for association rules, in Proc. 22nd Int. Conf. Very Large Data Bases, Morgan Kaufmann, pp. 134-145.
87. W. Wang, J. Yang and R. Muntz (1997), STING: A statistical information grid approach to spatial data mining, in Proc. 1997 Int. Conf. Very Large Data Bases (VLDB'97), Morgan Kaufmann, pp. 186-195.
88. G. Webb (2000), Efficient search for association rules, in Proc. of the 6th ACM-SIGKDD Int. Conf. Knowledge Discovery and Data Mining, ACM Press, pp. 99-107.
89. G. Williams (1990), Inducing and combining multiple decision trees, PhD thesis, Australian National University.
90. M. J. Zaki (1998), Efficient enumeration of frequent sequences, in 7th Int. Conf. Information and Knowledge Management, ACM Press, pp. 68-75.
91. M. J. Zaki (2000), Parallel sequence mining on shared-memory machines, in Large-Scale Parallel Data Mining (M. J. Zaki and C.-T. Ho, eds), Springer, pp. 161-189.
92. M. J. Zaki and C.-T. Ho, eds (2000), Large-Scale Parallel Data Mining, Vol. 1759 of Lecture Notes in Artificial Intelligence, Springer.
93. T. Zhang, R. Ramakrishnan and M. Livny (1996), BIRCH: An efficient data clustering method for very large databases, in Proc. 1996 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'96), ACM Press, pp. 103-114.