Data Mining and Knowledge Discovery Handbook, 2 Edition part 63 doc
Data Mining and Knowledge Discovery Handbook, 2 Edition part 130 doc
... 703Regression, 133, 514, 529 , 563linear, 95, 185, 21 0, 529 , 5 32, 564, 644,744, 121 2, 127 3logistic, 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 3stepwise, 189Regression,linear, ... 34, 88, 92, 94, 1 12, 135, 151, 163,795, 798, 881, 899, 907, 961, 9 72, 10 12, 1118, 1198, 127 3CART, 510CART (Classification and regression trees),34, 151, 163, 164, 22 0, 22 2, 22 4 22 6,899, 907, ... average(ARIMA), 122 , 527 , 1154, 1156AUTOCLASS, 28 3Average-link clustering, 27 9Bagging, 20 9, 22 6, 645, 744, 801, 881, 960,965, 966, 973, 1004, 121 1, 127 2, 127 3Bayes factor, 183Bayes’ theorem, 1 82 Bayesian...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 14 doc
... (Kaufman and Rousseeuw, 1990, Ng and Han, 1994, Ramaswamyet 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, Luet al., 20 03).Some of the above-mentioned classes are further ... 1998, Knorr et al., 20 00, Knorr et al., 20 01, Jin et al., 20 01, Breunig et al., 20 00, Williams et al., 20 02, Hawkins et al., 20 02, Bay and Schwabacher, 20 03). Another class of outlier detection...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 15 doc
... 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 Data Min-ing, 22 4 -22 8. ... R., ”Algorithms for mining distance-based outliers in largedatasets,” In Proc. 24 th Int. Conf. Very Large Data Bases (VLDB), 3 92- 403, 24 - 27 , 1998.7 Outlier Detection 127 median as a robust...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 23 doc
... 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 graphical models for discrete data. ... J. Hand, N. M. Adams, and R. J. Bolton. Pattern Detection and Discovery. Springer, NewYork, 20 02. D. J. Hand, H. Mannila, and P. Smyth. Principles of Data Mining. MIT Press, Cambridge, 20 01.T....
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 27 docx
... Equations 12. 17- 12. 18 with the following equations:minw,b,e1 2 w 2 +γ1 2 n∑i=1e 2 i( 12. 29)Subject toyi·((w ·Φ(xi)) + b)=1 −ei,i = 1, ,n( 12. 30)Important differences with standard ... (Equation 12. 25) while replacing the quadraticfunction in Equation 12. 26 with a linear function subject to constraints on the errorof kernel expansion (Equation 12. 25).Suykens et al. (20 02) introduced ... details and tricks can be found in the literature (Platt, 1998, 12 Support Vector Machines 24 7Ratsch G., Onoda T., and Muller K.R. Soft margins for AdaBoost. Machine Learning 20 01; 42( 3) :28 7– 320 .Rifkin...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 35 docx
... {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} ... alreadypresented before Eclat and its relatives.)The main difference in the Partition algorithm, compared to Apriori and Eclat, isthat the database is partitioned into several disjoint parts and the algorithm ... ascending orderimproves the distribution of the candidate sets within the used data structure (Borgelt and Kruse, 20 02) . Also, the number of candidate sets generated during the join stepcan...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 40 docx
... 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, beingjust a partial solution, so that the rule evaluation function is somewhat shortsighted(Freitas 20 01, 20 02a; Furnkranz & Flach 20 03).Another ... Algorithms (GAs) and Genetic Programming (GP). Then the chapter reviews the mainconcepts and principles used by EAs designed for solving several data mining tasks, namely: discovery of classification...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 43 docx
... 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 Kaufmann.Wong ML and Leung KS (20 00) Data Mining ... analysis. In: Liu H and Motoda H (Eds.) Feature Extraction,Construction and Selection: a data mining perspective, 393-406. Kluwer.Terano T and Inada M (20 02) Data mining from clinical data using interactive...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 46 doc
... ; 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 ConclusionsNeural networks are standard and important tools for data mining. Many featuresof neural networks such as nonlinear, data- driven, universal ... the data, but rather should study the problem and understand thenetwork models and the issues in various stages of model building, evaluation, and interpretation. 21 Neural Networks For Data Mining...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 63 doc
... (Sahar, 20 01), refers to measures of interest that can be applied automaticallyO. 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, 1990Keim D. A., Information Visualization and Visual Data mining, IEEE Transactions of Visu-alization and Computer Graphics, Vol. 7, No. 1, pp. 100-107, 20 02 Maimon O., and ... Srikant, R., Toivonen, H., and Verkamo, A. I. (1996). Advancesin Knowledge Discovery and Data Mining, chapter 12: Fast Discovery of AssociationRules, pages 307– 328 . AAAI Press/The MIT Press,...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 64 docx
... 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 Methods and Databases, pages 309– 320 , Cambridge, ... userO. Maimon, L. Rokach (eds.), Data Mining and Knowledge Discovery Handbook, 2nd ed., DOI 10.1007/978-0-387-09 823 -4_31, © Springer Science+Business Media, LLC 20 10 ...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 67 docx
... (REG C)163.90 10 5.064 20 .134 22 .575 22 .679 163.90 20 5.064 12. 813 12. 813 14.033163.90 30 5.064 9.7 62 10.103 10 .29 3163.90 40 5.064 8 .23 7 8 .23 7 8.5 42 163.90 50 5.064 7. 322 7.383 7.445163.90 60 ... the validation dataset, with 1 629 enterprises 32 64 Data Mining Model Comparison9 and to compare it with the previous ones see, for instance, (Zucchini, 20 00) or (Hand et al., 20 01).A possible ... mentioned is that Data Mining results must be of some consequence. This means that constant attention must be given tobusiness results achieved with the data analysis models. 32. 2 Data Mining Model...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 69 docx
... 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 and Knowledge Discovery Handbook. Chapter 16.7, ... (eds.), Data Mining and Knowledge Discovery Handbook, 2nd ed., DOI 10.1007/978-0-387-09 823 -4_34, © Springer Science+Business Media, LLC 20 10 668 Grigorios Tsoumakas, Ioannis Katakis, and Ioannis...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 73 doc
... 20 .60 728 858 27 .00680666Rank18 34.69 20 . 728 565 27 .70810968Rank16 33 .25 22 .996878 28 . 121 753 72 Rank13 31 .29 25 .26 048534 28 .27 61 028 6Rank14 34. 12 26.5717775 30.348 326 51Rank11 29 .79 32. 094 528 19 ... 46.47 27 .9 828 0 127 37 .22 45081Micmul08 51.00 23 .4813759 37 .24 028 758Micmul06 48.60 25 .94948057 37 .27 647059Rank08 22 . 42 52. 326 61 37.3 723 2476Micmul09 52. 71 22 .063535 32 37.38619949Micmul07 50.55 24 .3766 326 5 ... microaggregation and k-anonymity.35 Privacy in Data Mining 709method aPIL DR ScoreRank19 34.36 15.5 522 0303 24 .95390177Rank20 34.46 18.856305 26 .65 628 12 Rank17 35.88 17.66314794 26 . 773 729 12 Rank15 33.41 20 .60 728 858...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 75 doc
... The Knowledge- Acquisition Mode36 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 providesignificant savings in experimentation time.36 Meta-Learning 729 Chan, P., Stolfo, S. On the Accuracy of Meta-Learning for Scalable Data Mining. ... 19 92. Ali K., Pazzani M. J. Error Reduction Through Learning Model Descriptions. MachineLearning, 24 , 173 -20 2, 1996.Andersen, P., Petersen, N.C. A Procedure for Ranking Efficient Units in Data...
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