This tutorial on decision forests uses them for density estimation:
http://research.microsoft.com/en-us/groups/vision/decisionforests.aspx

Does this fit the bill for outlier detection?

On 28 February 2012 16:34, Nicholas Pilkington
<[email protected]> wrote:
> I was looking at it from a similar point of view to outlier detection using
> a single class SVM and wondering if the technique was applicable to decision
> to try and "explain" the data rather that perform classification/regression.
> Haven't looked at the literature but my point of reference was decision tree
> clustering of HMMs representing fragments of speech that are not tagged,
> i.e., building up the decision tree without class labels using BIC/MDL and
> then use it for classification. Like clustering.
>
>
> On Tue, Feb 28, 2012 at 4:24 PM, Olivier Grisel <[email protected]>
> wrote:
>>
>> 2012/2/28 Nicholas Pilkington <[email protected]>:
>> > Is there any easy was build up a decision tree for the purposes of
>> > clustering instead of for classification/regression?
>>
>> To me decision trees is intrinsically supervised algorithm. The split
>> of node of the tree is computed by a entropy measure w.r.t. the target
>> variable class distribution. How would you do that in an unsupervised
>> clustering case? Do you have any reference in the literature that
>> motivates this question?
>>
>> --
>> Olivier
>> http://twitter.com/ogrisel - http://github.com/ogrisel
>>
>>
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>
>
>
> --
> Nicholas C.V. Pilkington
> University of Cambridge
>
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