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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
148 Knowledge Discovery from Data Streams
m-dimensional space is given by:
vol (c
i
, m) =
π
m
2
r
m
Γ
m
2
+ 1
(9.3)
where Γ is the gamma function:
Γ
m
2
+ 1
=
m
2
!, for even m;
π
m!!
2
(m+1)/2
, for odd m.
(9.4)
This criterion may not be applicable to datasets with a large number of
attributes, once as m increases, vol (c
i
, m) tends to zero (Stibor et al.,
2006).
Sum of squares of distances between examples and centroid
divided by the number of examples. The sum of squares of distances
between examples belonging to c
i
and the centroid µ
i
is given by:
d (x
j
, µ
i
) =
X
x
j
c
i
(x
j
µ
i
)
2
(9.5)
Dividing it by the number of examples that belong to cluster c
i
, we
obtain a comparable ...
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Publisher Resources

ISBN: 9781439826126