Data Mining and Knowledge Discovery Handbook, 2 Edition part 117 pot

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 10.1007/9 78- 0- 387 -09 82 3 -4_4, © Springer Science+Business Media, LLC 20 10 50 Jerzy W. Grzymala-Busse and Witold J. Grzymala-Busse Grzymala-Busse ... such directions, and we cannot hope to uncover such structure using one dimensional projections. −1 −0 .8 −0.6 −0.4 −0 .2 0 0 .2 0.4 0.6 0 .8...

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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 Scie...

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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

... (Sonquist et al., 197 1), MAID (Gillo, 19 72) , THAID (Morgan and Messenger, 197 3) and CHAID (Kass, 198 0). CHAID (Chisquare–Automatic–Interaction–Detection) was originally designed to handle nominal ... in the main memory. Chan and Stolfo (199 7) suggest partitioning the datasets into several disjointed datasets, so that each dataset is loaded separately into the memory and use...

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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 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) , ... granulation. 22 Granular Computing and Rough Sets - An Incremental Development 447 and component based design and programming (Szyperski, 20 02) ), in artificial intel- ligenc...

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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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Data Mining and Knowledge Discovery Handbook, 2 Edition part 49 potx

Data Mining and Knowledge Discovery Handbook, 2 Edition part 49 potx

... scientists and engineers all O. Maimon, L. Rokach (eds.), Data Mining and Knowledge Discovery Handbook, 2nd ed., DOI 10.1007/978-0-387-09 823 -4 _23 , © Springer Science+Business Media, LLC 20 10 22 Granular ... modeling data and thus plays a central role in the pro- cess of knowledge discovery and data mining. Data mining tasks, in these days, require fast...

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Data Mining and Knowledge Discovery Handbook, 2 Edition part 51 pot

Data Mining and Knowledge Discovery Handbook, 2 Edition part 51 pot

... (Handl and Meyer B, 20 02, Hoe et al., 20 02, Ramos and Merelo, 20 02) . Many authors (Handl and Meyer B, 20 02, Ramos et al., 20 02) proposed a number of modifica- tions to improve the convergence rate and ... H: ϕ T (x i ). ϕ (x j )=[x 2 i,1 , √ 2. x i,1 x i ,2 ,x 2 i ,2 ].[x 2 j,1 , √ 2. x j,1 .x j ,2 ,x 2 j ,2 ] T . Swagatam Das and Ajith Abraham 23 Pattern Cl...

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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 54 potx

Data Mining and Knowledge Discovery Handbook, 2 Edition part 54 potx

... Komem and Moti Schneider, On the Use of Fuzzy Logic in Data Mining, in The Data Mining and Knowledge Discovery Handbook, O. Maimon, L. Rokach (Eds.), pp. 517-533, Springer, 20 05. Maher P. E. and ... Information and Knowledge Systems, Lecture Notes in Computer Science, Springer, pp. 178-196, 20 02. Maimon, O. and Rokach, L., Decomposition Methodology for Knowledge...

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Data Mining and Knowledge Discovery Handbook, 2 Edition part 117 pot

Data Mining and Knowledge Discovery Handbook, 2 Edition part 117 pot

... (59. 12) , with the l i denoting the eigenvalues. −→ z k = S − 1 2 k ( −→ f k − −→ f k ) S − 1 2 k = ΓΛ − 1 2 Γ (59. 12) where −→ f k is the observed frequency vector and Λ − 1 2 = diag(l − 1 2 i ) The ... (Liebich et al., 20 02) , TransCOM- PEL (Margoulis et al., 20 02) , LocusLink, OMIM, and RefSeq (Wheeler et al., 20 04). Sites are experimentally proven for their inclusio...

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