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DATA MINING Concepts_ Methods_ Models_ and Algorithms

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DATA MINING Concepts_ Methods_ Models_ and Algorithms Powered By Docstoc
					DATA MINING: Concepts, Methods, Models, and Algorithms
Author: Mehmed Kantardzic

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Pg. 22 Paragraph 3

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For example, for a twodimensional space with 10000 points the expected distance is D(2,10000) = 0.0005 and for a 10-dimensional space with the same number of sample points D(10,10000) = 0.4. 7 2 = 1/8   ( xi – 4.375)2 i=1 Use an appropriate nonlinear transformation (one of those represented in Figure 4.5) to improve regression results... Info(S) = – …….. Infox1(T)=5/13 (–2/5 log2 (2/5) – 3/5 log2 (3/5)) + 3/13 (–3/3 log2 (3/3) –0/3 log2 (0/3)) + 5/14 (–3/5 log2 (3/5) – 2/5 log2 (2/5)) = 0.747 bits

Corrected Text (bold typed are corrections)
For example, for a two-dimensional space with 10000 points the expected distance is D(2,10000) = 0.005 and for a 10dimensional space with the same number of sample points D(10,10000) = 0.4.

Pg. 95 Formula 2

8 2 = 1/7   ( xi – 4.375)2 i=1 Use an appropriate nonlinear transformation (one of those represented in Table 5.3) to improve regression results... Info(T) = – ………. Infox1(T)=5/13 (–2/5 log2 (2/5) – 3/5 log2 (3/5)) + 3/13 (–3/3 log2 (3/3) –0/3 log2 (0/3)) + 5/13 (–3/5 log2 (3/5) – 2/5 log2 (2/5)) = 0.747 bits

Pg. 114 Problem 4 c)

Pg. 144 Formula 1

Pg. 151 Formula 1

Pg. 161 d2 Figure 7.13a) b2

Pg. 244 Problem 7.

S3 = ( * * *1 * * *)

S3 = ( * * 1 * * *)

Pg. 95 Figure 5.1

Corrected content (bold) of the Figure 5.1 (other parts are the same).

Max 5 2 Mean -2 Min

Pg.107 Formula 5.

p = e –0.6 / ( 1 + e –0.6) = 0.35

p = e 0.6 / ( 1 + e 0.6) = 0.35

Pg. 240

( x * * * *s * ) : C1
Formula 3.

( x * * * s * ) : C1

Pg. 273 Problem 5.

C(x) = x2 / 24

C(x) = x2 / 24 C(x) = 1

for 0  x  4 for 4 < x  10.

Pg. 255 Formula 5.

Nec(A, B) = min [max{(0,0.5, 0.8, 1.0, 0.2), (0.9, 0.4, 0.3, 0.1, 0)}] = min [0.1, 0.6, 0.8, 1.0, 1.0] = 0.1

Nec(A, B) = min [max {(0, 0.5, 0.8, 1.0, 0.2), (0.1, 0.6, 0.7, 0.9, 1)}] = min [0.1, 0.6, 0.8, 1.0, 1.0] = 0.1


				
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