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

[Christian S. <st...@Ma...>]

Dear Michael,

the ESOM - algorithm is indeed non-convergent. Every map you train will be
unique. This is because of the random initialization of the map and the
(optional) permutation of the input data during the training process. The
overall structure of the map will be similar, but inter as will as intra
cluster neighbourhoods may be twisted or sorted in another fashion,
without though beeing less meaningful. e.g. the U-Matrix view on the map
will unveal where large or low distances are present.

welcome to the user community!

mfg Christian

Michael Dell Junior said:
> Dear Mario  & Fabian,
>
>
> Thank you very much for your valuable comments and prompt response. I will
> implement your recommendations on an immediate basis.
>
>
> I have one other request pertaining to the training using the Databionics
> ESOM tool. What are the actions one needs to take if with two *identical
> runs* (in terms of parameter selection, and training set) obtains a
> different end result in terms of the proximity/clustering of different
> instances to one another? (I am aware that the Visual Part might be
> different on each run but my expectation would be that that the underlying
> structural sorting of the instances should be the same. e.g. In Run 1,
> Instance "234" is surrounded by instances "456", "789" & "123". Shouldn't
> the same "234" instance be surrounded by the same "456", "789" & "123"
> instances in Run 2? )
>
>  Is this a sign of non-convergence? Is this a sign of some other
> underlying process that I am not aware of? Is there a random component
> that I am not aware of?
>
> Your comments and suggestion on this issue will be very much appreciated.
>
>
> Regards,
> Michael
>
>
>
>
>
> ---------------------------------
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