Re: [Databionic-ESOM-User] Re: Parameter Selection & ...More

[<fa...@in...>]

forwarded from Alfred Ultsch:

>
From the cases we have seen such data are typically placed within a
little funnel- like structure if there are more than one in close
neighborhood.  On a P-Matrix the densities would be rather low,
however on a U-Matrix they would lie in a valley. So I would try the
standard workhorse ESOM with it.
<

> 3) My last question is a little bit more conceptual in nature. Most of
> the literature on ESOM's and the visualization development that
> accompanies the literature, focuses on the objective of identifying
> regions of high density that will eventually be identified as clusters.
>  
> I am more interested in the topological assortment of instances
> according to a degree of similarity provided by the ESOM and not
> necessarily in the formation of a high-density cluster (whatever the
> density definition might be). So, in U-matrix terms, my interest might
> lie more on the mountains and less on the valleys, as the area I am
> researching deals more with rare events. However, it often appears that
> these "rare events" occur in areas of  low density (however that is
> defined) but at the same time appear to be *adjacent* to each other on
> the 2-D map. In U-matrix terms, this would be a series of mountains on
> the map that would form a mountain range. This mountain range might be
> of immense interest to my research.
>  
>  
> Would you happen to have any understanding/awareness as to:
>  
> 1) What kind of parameter tuning can best sort out / display these "rare
> instances"? Would it be any different from the the current methodology,
> in your opinion?
>  
> 2) The availability of any scientific literature that deals with this issue?

not really. i know of a paper using SOM for novelty detection [1],
however. SOM are trained with normal data and novelties are recognized
by the projection error. is this what you are looking for? we have not
implemented displays for projection errors, but it would be rather
straight forward to do so.

bye
fabian

[1] A. Ypma and R.P.W. Duin, Novelty detection using Self-Organizing
Maps, rogress in Connectionist-Based Information Systems - Proceedings
of ICONIP97, Dunedin (New-Zealand), 1997