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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
94 Knowledge Discovery from Data Streams
tests. At any time, if τ >
k
, the system overrules the criterion of Equation 6.4,
assuming the leaf has been fed with enough examples, hence it should consider
the highest distance to be the real diameter.
Expanding the Tree. When a split point is reported, the pivots are variables
x
1
and y
1
where d
1
= d(x
1
, y
1
), and the system assigns each of the remaining
variables of the old cluster to the cluster which has the closest pivot. The
sufficient statistics of each new cluster are initialized. The total space required
by the two new clusters is always less than the one required by the previous
cluster. Algorithm 16
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Publisher Resources

ISBN: 9781439826126