Data Mining and Knowledge Discovery Handbook, 2 Edition part 124 pot
Data Mining and Knowledge Discovery Handbook, 2 Edition part 124 pot
... requiring a com-
bination of tools from Data Mining, operations research, design of experiments, direct and
database marketing, database technologies, and others. And we have not discussed here the
organizational ... targeting
models.
63.7 .2 Data Pitfalls
Data Bias
By data bias we mean that not all observations in the database have the same items of data, with
certain segmen...
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...
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
Scient...
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...
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...
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)...
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...
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...
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...
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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