R help - Mar 2013 - hierarchical clustering with pearson's coefficient

Hello,

I want to use pearson's correlation as distance between observations and
then use any centroid based linkage distance (ex. Ward's distance)

When linkage distances are formed as the Lance-Williams recursive
formulation, they just require the initial distance between observations.
See here: http://en.wikipedia.org/wiki/Ward%27s_method

It is said that you have to use euclidean distance between the initial
observations. However i have found this:

http://research.stowers-institute.org/efg/R/Visualization/cor-cluster/

where they use pearson's correlation for hierarchical clustering.

Any idea if anything is violated in case pearson's correlation is used with
Ward's linkage function?

the dissimilarity of pearson's correlation can be defined as d
sqrt(1-pearsonsimilarity^2). can that be considered as norm1 distance? and
thus norm2 if we square it? so that the wikipedia's statement "To apply
a
recursive algorithm under this objective function, the initial distance
between individual objects must be (proportional to) squared Euclidean
distance." is valid?

Best,
Pierre

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Anyone for that question?




2013/3/28 Pierre Antoine DuBoDeNa <padbdn@gmail.com>
> Hello,
>
> I want to use pearson's correlation as distance between observations
and
> then use any centroid based linkage distance (ex. Ward's distance)
>
> When linkage distances are formed as the Lance-Williams recursive
> formulation, they just require the initial distance between observations.
> See here: http://en.wikipedia.org/wiki/Ward%27s_method
>
> It is said that you have to use euclidean distance between the initial
> observations. However i have found this:
>
> http://research.stowers-institute.org/efg/R/Visualization/cor-cluster/
>
> where they use pearson's correlation for hierarchical clustering.
>
> Any idea if anything is violated in case pearson's correlation is used
> with Ward's linkage function?
>
> the dissimilarity of pearson's correlation can be defined as d >
sqrt(1-pearsonsimilarity^2). can that be considered as norm1 distance? and
> thus norm2 if we square it? so that the wikipedia's statement "To
apply a
> recursive algorithm under this objective function, the initial distance
> between individual objects must be (proportional to) squared Euclidean
> distance." is valid?
>
> Best,
> Pierre
>
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I am not sure about your question but i did find this:

http://research.med.helsinki.fi/corefacilities/proteinchem/hierarchical_clustering_basics.pdf

it seems to address all three topics so perhaps the answer is in there??


On Mar 28, 2013, at 6:16 PM, Pierre Antoine DuBoDeNa wrote:
> Hello,
> 
> I want to use pearson's correlation as distance between observations
and
> then use any centroid based linkage distance (ex. Ward's distance)
> 
> When linkage distances are formed as the Lance-Williams recursive
> formulation, they just require the initial distance between observations.
> See here: http://en.wikipedia.org/wiki/Ward%27s_method
> 
> It is said that you have to use euclidean distance between the initial
> observations. However i have found this:
> 
> http://research.stowers-institute.org/efg/R/Visualization/cor-cluster/
> 
> where they use pearson's correlation for hierarchical clustering.
> 
> Any idea if anything is violated in case pearson's correlation is used
with
> Ward's linkage function?
> 
> the dissimilarity of pearson's correlation can be defined as d >
sqrt(1-pearsonsimilarity^2). can that be considered as norm1 distance? and
> thus norm2 if we square it? so that the wikipedia's statement "To
apply a
> recursive algorithm under this objective function, the initial distance
> between individual objects must be (proportional to) squared Euclidean
> distance." is valid?
> 
> Best,
> Pierre
> 
> 	[[alternative HTML version deleted]]
> 
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