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Knowledge Discovery from Data Streams
book

Knowledge Discovery from Data Streams

by Joao Gama
May 2010
Intermediate to advanced content levelIntermediate to advanced
255 pages
8h 11m
English
Chapman and Hall/CRC
Content preview from Knowledge Discovery from Data Streams
190 Knowledge Discovery from Data Streams
Algorithm 31: Local L2 Thresholding.
Input of peer p
i
: , L, X
i,i
, N
i
, l
Global constants: A random seed s
Data structure for p
i
: For each p
j
N
i
X
i,j
, |X
i,j
|, X
j,i
, |X
j,i
|,
last message
Output of peer p
i
: 0 if ||K
i
|| < , 1 otherwise
Computation of <
F
:
- Let R
in
= {~x : ||~x|| }
- Let ˆu
1
, . . . , ˆu
l
be pseudo-random unit vectors
- Let H
j
= {~x : ~x · ˆu
j
}
- Let <
F
= {R
in
, H
1
, . . . , H
l
, T }
Computation of X
i,j
and |X
i,j
|:
|X
i,j
|
|K
i
|K
i
−|X
j,i
|X
j,i
|K
i
|−|X
j,i
|
w |X| |K
i
| |X
j,i
|
while (A
i,j
/ R
F
(K
i
)orW
i,j
/ R
F
(K
i
)and|W
i,j
| 6= 0) do
w |w/2| and |X
i,j
|K
i
| |X
j,i
| w|
Initialization: last message −∞, computeR ...
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Publisher Resources

ISBN: 9781439826126