Data Mining and Knowledge Discovery Handbook, 2 Edition part 52 pot
Data Mining and Knowledge Discovery Handbook, 2 Edition part 52 pot
... ( 62. 09) 40 023 .25 (43. 92) 35 024 .35 (101 .26 ) 34 923 .22 (24 .28 ) Japanese Vowel 40.00 4 029 3.75 (23 .33) 28 291.60 ( 121 . 72) 29 014.85 (21 .67) 24 023 .95 (20 . 62) Choice of Parameters for MEPSO The ... 393 92. 05 (7 .20 ) 423 22. 05 (2. 33) 3 526 7.45 (9.11) Glass 65.00 47 625 .35 (6.75) 403 82. 15 (7 .27 ) 3 829 2 .25 (10. 32) 37 627 .05 (...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 8 potx
... Rokach (eds.), Data Mining and Knowledge Discovery Handbook, 2nd ed., DOI 10.1007/978-0-387-09 823 -4_4, © Springer Science+Business Media, LLC 20 10 50 Jerzy W. Grzymala-Busse and Witold J. Grzymala-Busse Grzymala-Busse ... Newsletter 4 (20 02) 21 – 30. Wu X. and Barbara D. Modeling and imputation of large incomplete multidimensional datasets. Proc. of the 4-th Int. Con...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 13 pot
... 131–158. Rokach, L. and Maimon, O., Clustering methods, Data Mining and Knowledge Discovery Handbook, pp. 321 –3 52, 20 05, Springer. Rokach, L. and Maimon, O., Data mining for improving the quality of manufacturing: ... Manufacturing, 17(3) :28 5– 29 9, 20 06, Springer. Rokach, L., Maimon, O., Data Mining with Decision Trees: Theory and Applications, World Scien...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 18 pot
... y=c 1 S σ y=c 1 S − σ a i ∈dom 1 (a i )AND y=c 2 S σ y=c 2 S This measure was extended in (Utgoff and Clouse, 1996) to handle target at- tributes with multiple classes and missing data values. Their ... programming (Duda and Hart, 1973,Bennett and Mangasarian, 1994), linear discriminant analysis (Duda and Hart, 1973,Friedman, 1977,Sklans...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 19 potx
... (Crawford et al., 20 02) . Decision trees are useful for many application domains, such as: Manufacturing lr18,lr14, Security lr7,l10 and Medicine lr2,lr9, and for many data mining tasks, 9 Classification ... entire dataset. However, this method also has an upper limit for the largest dataset that can be processed, because it uses a data structure that scales with the dataset size...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 21 pot
... quan- tity: p(D|M hi )= 1 (2 π ) n /2 detR 1 /2 io detR 1 /2 in Γ ( ν in /2) Γ ( ν io /2) ( ν io σ 2 io /2) ν io /2 ( ν in σ 2 in /2) ν in /2 and the parameters are specified by the next updating rules: α i1n = ν io /2 +n /2 1/ α i2n =(− β T in R in β in + ... Abad, and Marco F. Ramoni and the marginal likelihood of each local dependency is p(D|M hi )= Γ ((n −p(i)...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 26 pot
... and Chapelle, 20 00). O. Maimon, L. Rokach (eds.), Data Mining and Knowledge Discovery Handbook, 2nd ed., DOI 10.1007/978-0-387-09 823 -4_ 12, © Springer Science+Business Media, LLC 20 10 ... the same linear 23 0 Richard A. Berk Dasu, T., and T. Johnson (20 03) Exploratory Data Mining and Data Cleaning. New York: John Wiley and Sons. Christianini, N and J. Sh...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 33 pot
... basket data (Agrawal and Srikant, 1995) • Causes of plan failures (Zaki, 20 01) • Web personalization (Mobasher et al., 20 02) • Text data (Brin et al., 1997A,Delgado et al., 20 02) • Publication databases ... antecedent X 2 being as large as possible (or 15 Association Rules 303 {1, 2, 3} {1, 2} {1, 3} {2, 3} {1} {2} {3} { } 3 23 123 root 3 Fig. 15.1. Itemset lattice and...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 37 potx
... ACDE BCDE ACE ADE ABDE BCE BDE CDE DECEBC BD BE EDCB O / 2 2 3 2 4 3 32 4 22 3 1 12 543 22 2 3 111 11 11 11 D = TID Transaction 1 ABCDE 2 ABCD 3 ABE 4 ACD 5 CD 6 CE Fig. 17.1. This figure shows the ... Boulicaut and Bykowski, 20 00)), the frequent free itemsets and the δ -free itemsets (Boulicaut et al., 20 00,Boulicaut et al., 20 03), the disjunction-free sets (Bykowski and...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 47 potx
... ; 21 :20 3 -22 4. Klein B.D., Rossin D. F. (1999), Data quality in neural network models: effect of error rate and magnitude of error on predictive accuracy. Omega ; 27 :569-5 82. Kohonen T. (19 82) , ... on non-partition theory,but only in an epsilon step away from partitioning method. 22 .2. 2 Knowledge Level Processing and Computing with Words The information in each granule is...
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