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Distance and Dissimilarity Measures
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Partitioning Data into Clusters
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EuclideanDistance
SquaredEuclideanDistance
HammingDistance
ChebyshevDistance
BrayCurtisDistance
CanberraDistance
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Exploratory Data Analysis
Distance and Dissimilarity Measures
New in 6.0: Mathematics & Algorithms
New in 6.0: Statistics
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ManhattanDistance
ManhattanDistance
[
u
,
v
]
gives the Manhattan or "city block" distance between vectors
u
and
v
.
MORE INFORMATION
ManhattanDistance
[
u
,
v
]
is equivalent to
Total
[
Abs
[
u
-
v
]]
.
»
EXAMPLES
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Basic Examples
(2)
Manhattan distance between two vectors:
In[1]:=
Out[1]=
Manhattan distance between numeric vectors:
In[1]:=
Out[1]=
Scope
(2)
Applications
(2)
Properties & Relations
(5)
SEE ALSO
EuclideanDistance
SquaredEuclideanDistance
HammingDistance
ChebyshevDistance
BrayCurtisDistance
CanberraDistance
TUTORIALS
Partitioning Data into Clusters
MORE ABOUT
Exploratory Data Analysis
Distance and Dissimilarity Measures
New in 6.0: Mathematics & Algorithms
New in 6.0: Statistics
New in 6
© 2008 Wolfram Research, Inc.
EXAMPLES
Basic Examples 
Scope 