Data Mining and Knowledge Discovery Handbook, 2 Edition part 63 doc

... 703 Regression, 133, 514, 529 , 563 linear, 95, 185, 21 0, 529 , 5 32, 564, 644, 744, 121 2, 127 3 logistic, 97, 21 8, 22 6, 527 , 531, 5 32, 645, 647, 849, 850, 10 32, 1154, 120 0, 120 1, 120 5, 121 2, 127 3 stepwise, 189 Regression,linear, ... 34, 88, 92, 94, 1 12, 135, 151, 163, 795, 798, 881, 899, 907, 961, 9 72, 10 12, 1118, 1198, 127 3 CART, 510 CART (Classification and regressio...

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... (Kaufman and Rousseeuw, 1990, Ng and Han, 1994, Ramaswamy et al., 20 00, Barbara and Chen, 20 00, Shekhar and Chawla, 20 02, Shekhar and Lu, 20 01, Shekhar and Lu, 20 02, Acuna and Rodriguez, 20 04). Hu and ... the entire population (Schiffman et al., 1981,Ng and Han, 1994, Shekhar and Chawla, 20 02, Shekhar and Lu, 20 01, Shekhar and Lu, 20 02, Lu et al., 20...

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... Fifth International Conference and Data Warehousing and Knowledge Discovery (DaWaK 02) , Aix en Provence, France, 20 02. Haining R., Spatial Data Analysis in the Social and Environmental Sciences. ... 19 92. Johnson T., Kwok I., Ng R., ”Fast Computation of 2- Dimensional Depth Contours,” In Pro- ceedings of the Fourth International Conference on Knowledge Discovery and...

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... Rokach (eds.), Data Mining and Knowledge Discovery Handbook, 2nd ed., DOI 10.1007/978-0-387-09 823 -4_11, © Springer Science+Business Media, LLC 20 10 20 8 Paola Sebastiani, Maria M. Abad, and Marco ... Madigan and G. Ridgeway. Bayesian data analysis for Data Mining. In Handbook of Data Mining, pages 103–1 32. MIT Press, 20 03. D. Madigan and J. York. Bayesian g...

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... Equations 12. 17- 12. 18 with the following equations: min w,b,e 1 2  w  2 + γ 1 2 n ∑ i=1 e 2 i ( 12. 29) Subject to y i ·((w · Φ (x i )) + b)=1 −e i , i = 1, ,n ( 12. 30) Important differences with standard ... (Equation 12. 25) while replacing the quadratic function in Equation 12. 26 with a linear function subject to constraints on the error of kernel expansion (Equation 12. 25)...

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... {100 ,20 0} 2 50% {chips} {100 ,20 0,400} 3 75% {pizza} {300,400} 2 50% {wine} {100,300} 2 50% {beer,chips} {100 ,20 0} 2 50% {beer,wine} {100} 1 25 % {chips,pizza} {400} 1 25 % {chips,wine} {100} 1 25 % {pizza,wine} ... already presented before Eclat and its relatives.) The main difference in the Partition algorithm, compared to Apriori and Eclat, is that the database is parti...

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... greedy data mining methods (Freitas 20 02a; Dhar et al. 20 00; Papagelis & Kalles 20 01; Freitas 20 01, 20 02c). O. Maimon, L. Rokach (eds.), Data Mining and Knowledge Discovery Handbook, 2nd ed., ... candidate conditions, the rule is still incomplete, being just a partial solution, so that the rule evaluation function is somewhat shortsighted (Freitas 20 01, 20 02a...

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... Rokach (eds.), Data Mining and Knowledge Discovery Handbook, 2nd ed., DOI 10.1007/978-0-387-09 823 -4 _20 , © Springer Science+Business Media, LLC 20 10 408 Oded Maimon and Shahar Cohen 20 .4 Extensions ... Construction and Selection, 307- 323 . Kluwer. Witten IH and Frank E (20 05) Data Mining: practical machine learning tools and techniques . 2nd Ed. Morgan Kaufma...

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... ; 9 :2- 54. Chen K.Y., Wang, C.H. (20 07), Support vector regression with genetic algorithms in fore- casting tourism demand. Tourism Management ; 28 :21 5 -22 6. Chiang W.K., Zhang D., Zhou L. (20 06), ... and Lisboa, Edisbury, and Vellido (20 00). 21 .5 Conclusions Neural networks are standard and important tools for data mining. Many features of neural networks such as nonlinear...

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... (Sahar, 20 01), refers to measures of interest that can be applied automatically O. Maimon, L. Rokach (eds.), Data Mining and Knowledge Discovery Handbook, 2nd ed., DOI 10.1007/978-0-387-09 823 -4_30, ... 18, pp. 21 63 – 21 70, 1990 Keim D. A., Information Visualization and Visual Data mining, IEEE Transactions of Visu- alization and Computer Graphics, Vol. 7, No. 1,...

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... Data, pages 26 5 27 6, Tucson, AZ, USA. Fayyad, U. M., Piatetsky-Shapiro, G., and Smyth, P. (1996). Advances in Knowledge Dis- covery and Data Mining, chapter 1: From Data Mining to Knowledge Discovery: ... Roddick, J. F. (20 00). Higher order mining: Modeling and mining the results of knowledge discovery. In Proceedings of the Second Conference on Data Mining Me...

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... (REG C) 163.90 10 5.064 20 .134 22 .575 22 .679 163.90 20 5.064 12. 813 12. 813 14.033 163.90 30 5.064 9.7 62 10.103 10 .29 3 163.90 40 5.064 8 .23 7 8 .23 7 8.5 42 163.90 50 5.064 7. 322 7.383 7.445 163.90 60 ... the validation dataset, with 1 629 enterprises 32 64 Data Mining Model Comparison 9 and to compare it with the previous ones see, for instance, (Zucchini, 20 00) or...

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... extension to SQL for mining association rules. Data Mining and Knowledge Discovery, 2( 2):195 22 4, 1998. K. Morik and M. Scholz. The Mining Mart approach to knowledge discovery in databases. In Intelligent ... In- ductive Queries, volume 26 82 of LNCS, pages 3 26 . Springer-Verlag, 20 04. J F. Boulicaut and B. Jeudy. Constraint-based Data Mining. In Data Mining...

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... 20 .60 728 858 27 .00680666 Rank18 34.69 20 . 728 565 27 .70810968 Rank16 33 .25 22 .996878 28 . 121 753 72 Rank13 31 .29 25 .26 048534 28 .27 61 028 6 Rank14 34. 12 26.5717775 30.348 326 51 Rank11 29 .79 32. 094 528 19 ... 46.47 27 .9 828 0 127 37 .22 45081 Micmul08 51.00 23 .4813759 37 .24 028 758 Micmul06 48.60 25 .94948057 37 .27 647059 Rank08 22 . 42 52. 326 61 37.3 723 2...

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... The Knowledge- Acquisition Mode 36 Meta-Learning 723 tree imbalance, etc.), as a means to characterize the dataset (Bensusan, 1998, Bensusan and Giraud-Carrier, 20 00b, Hilario and Kalousis, 20 00, ... of Data Mining tools and in particular to provide significant savings in experimentation time. 36 Meta-Learning 729 Chan, P., Stolfo, S. On the Accuracy of Meta-Learning for Scala...

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