R devel - Jul 2011 - fuzzy c-means algorithm in e1071

I was wondering if it was possible to add an extra option to the cmeans
function please?  In a recent paper by Moore et al.,(2009) they state:

"The fuzzy membership function is: 1 - F(Z2), where Z2 is a squared
Mahalanobis distance and F is a cumulative chi-square distribution.  The
Mahalanobis distance is the multivariate equivalent of the standardised
random variable Z = (X - M)/S, which is the distance of the univariate
random variable X from its mean M normalised by the standard deviation S. 
In other words, the Mahalanobis distance is a weighted form of the
Euclidean, and is preferable because it incorporates the shape of the the
distribution of points around the cluster centre (i.e., the geometric shape
of the point cloud expressed in terms of variance). The fuzzy membership
ranges from 0 to 1 ..."

so as well as having a dist = "euclidean" option, would it be possible
to
have a dist = "mahalanobis" option please?  I realise that this is
quite an
ask - you could even point me in the right direction of where to start
editing code and getting it compiled on my own machine.

Tim Smyth

ref: T.S. Moore et al. Remote Sensing of Environment 113 (2009) 2424-2430 

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View this message in context:
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Since you are talking about a contributed package: Please send feature 
requests to the package maintainer:

 > maintainer("e1071")
[1] "Friedrich Leisch <Friedrich.Leisch at R-project.org>"


It is even a better idea to send him your own code suggestions so that 
it will be quicker to add the requested features to the package.

Best,
Uwe Ligges




On 15.07.2011 10:11, Tim Smyth wrote:
> I was wondering if it was possible to add an extra option to the cmeans
> function please?  In a recent paper by Moore et al.,(2009) they state:
>
> "The fuzzy membership function is: 1 - F(Z2), where Z2 is a squared
> Mahalanobis distance and F is a cumulative chi-square distribution.  The
> Mahalanobis distance is the multivariate equivalent of the standardised
> random variable Z = (X - M)/S, which is the distance of the univariate
> random variable X from its mean M normalised by the standard deviation S.
> In other words, the Mahalanobis distance is a weighted form of the
> Euclidean, and is preferable because it incorporates the shape of the the
> distribution of points around the cluster centre (i.e., the geometric shape
> of the point cloud expressed in terms of variance). The fuzzy membership
> ranges from 0 to 1 ..."
>
> so as well as having a dist = "euclidean" option, would it be
possible to
> have a dist = "mahalanobis" option please?  I realise that this
is quite an
> ask - you could even point me in the right direction of where to start
> editing code and getting it compiled on my own machine.
>
> Tim Smyth
>
> ref: T.S. Moore et al. Remote Sensing of Environment 113 (2009) 2424-2430
>
> --
> View this message in context:
http://r.789695.n4.nabble.com/fuzzy-c-means-algorithm-in-e1071-tp3669374p3669374.html
> Sent from the R devel mailing list archive at Nabble.com.
>
> ______________________________________________
> R-devel at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-devel