Supervised:  all samples are labeled (e.g. by disease subtype)
Semi-supervised:  only some of the samples are labeled, or the labels are
expressed in terms of probabilities
Unsupervised: there are no labels and the method of choice is supposed to
discover them

>From the above I hope it will be clear that partitioning around medioids,
self-organizing maps, and k-means are all unsupervised.

ANOVA, SVMs, multinomial logistic regression, and linear discriminant
analysis are examples of supervised methods.

For a useful paper describing differences between the two, and a
model-based semi-supervised approach, you might like this JSS
paper.<http://www.jstatsoft.org/v47/i03>

A far more in-depth treatment can be freely perused at the website for
Elements of Statistical Learning (2nd
edition)<http://www-stat.stanford.edu/~tibs/ElemStatLearn/>
.

Hope this helps.

--t



On Wed, Apr 25, 2012 at 8:26 PM, wenhuo hu <huwenhuo@gmail.com> wrote:

> Hi all,
>
> I am recently analyzing the array data. There are several groups represent
> different disease subtype. I will just describe what I did here. I
> identified significant genes. And extract the expression levels of these
> genes, and performed the cluster analysis using gplots package in
> bioconductor/R. The question I have here is the cluster analysis did not
> group the samples well according the disease subtype. So I assume this is a
> question about supervised and unsupervised cluster. From online data, it
> seems this not really right, because supervised analysis describe more
> likely the way to classify new samples based on previous data. And there
> come with the idea of semi-supervised concept. Here I am already confused.
> Would the analysis methods, such as PAM, SOM, and Kmeans, be supervised or
> semi-supervised clusters? Could anyone spend time to clear my idea about
> supervised, semi-supervised, and unsupervised? And recommend any packages
> in bioconductor that might help me to group the samples according disease
> sub-type?
>
> I like programming, and have biology/medicine background, with relatively
> limited bioinformatics. Any interpretation are welcome.
>
> Thanks!
>
>
> Wenhuo Hu
> Park lab
>
> Memorial Sloan Kettering Cancer Center
> Zuckerman Research Building
> 408 East 69th Street
> Room ZRC-527
> New York, NY 10065
> Phone 646-888-3220
> huw@mskcc.org
>
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>
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>



-- 
*A model is a lie that helps you see the truth.*
*
*
Howard Skipper<http://cancerres.aacrjournals.org/content/31/9/1173.full.pdf>

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