Data Mining and Knowledge Discovery Handbook, 2 Edition part 21 pot

Data Mining and Knowledge Discovery Handbook, 2 Edition part 21 pot

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 8 potx

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. Co...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 13 pot

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

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

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 26 pot

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

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

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

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 i...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 48 potx

Data Mining and Knowledge Discovery Handbook, 2 Edition part 48 potx

... 22 .3, we have Table 22 .4 and Table 22 .5; they are isomorphic. Table 22 .5 provides the topology of Table 22 .4. Table 22 .4 and 22 .5 provide a better interpretation than that of Table 22 .2 and 22 .3. 458 ... use the same partition and naming scheme as in the previous sec- tion; so the third column is exactly the same as that in Table 22 .1. The results are shown in Tab...
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