Re: [Fwd: Re: [Databionic-ESOM-User] ESOM]

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

mit...@we... wrote:
> Dear Mr. Mörchen and Mr. Efthymiou  ,
> I really appreciate your response which did help me understand and make
> clear some things but I still have some questions and problems with ESOM.
> 
> I tried to follow the steps of your guidelines but I have some questions.
> 
> - create two seperate *.lrn for training and test data
> 
> ok but does the training test data have to include a column with the class
> of each sample or it is the same with having a separate .cls file. (in
> some example .lrn files there is a column for the class of the data)

the training should not be performed using class information, it is an
unsupervised process, after all. creating a class mask for the esom can
be done without any prior classification.

having a *.cls file for the training data comes in handy when you are
actually searching for clusters corresponding or at least similar to
some known classes. displaying these known classes as the best match
color might help you in finding a better class mask corresponding to
your ground truth. if the known classes do not correspond to the bm
positions and the distance structures at all, they might not be related
to the data vectors.

> Is it necessary one of the fields of the .lrn file to be labeled as unique
> key (9)?

we highly recommend using a unique key column (9) in the *.lrn files,
because this key will be used to map bestmatches to data vectors and
classification labels. the class column in *.lrn files is deprecated and
should not be used.

> 
> -train ESOM with training data
> ok
> 
>  - optional: load *.cls with known classification of training data
> the *.cls file should be loaded before or after the training process. (I
> suppose before)
> It is loaded from the tab classes or class mask?

no, afterwards as explained above. the classes tab.

>>- identify clusters and create class mask (also *.cls)
> 
> how do I identify clusters?? and create class mask?
> Do I use the classify selection from the tools menu?
> for some reason it doesn't seem to work although I press the start button
> the procedure thoes not start and no output .cls file is created.

you can identify distance based clusters with the U-Matrix display.
depending on the color gradient used (e.g. gray) dark regions correspond
to valleys, i.e. core cluster regions and light regoins correspond to
mountains, i.e. cluster boundaries.

the class masks are created with the 3rd toolbar button.

only if bestmatches and a classmask are loaded you can classify the
bestmatches according to their positions and possible masks in that
area. the result will be displayed in the classes tab.

>  - load *.lrn with test data
> ok
> 
>>- project this data on ESOM
> 
> ok
> 
>>- save newly created *.cls for test data
> 
> How do I create the new *.cls data

see above.

> 
>>- optional: analyze *.cls for test data, e.g. compare to *.cls with
>>known classification of test data.
>>
>> we offer no tools for the last step which is rather easy however. i
>> could post some matlab code, if you wish.

> Yes please send me the matlab code (e-mail mit...@un...)

% using loadcls that ships with the ESOM tools
true_cls = loadcls('true.cls');
esom_cls = loadcls('esom.cls');

% check if key columns are the same
if sum(true_cls(:,1)~=esom_cls(:,1))>0
  error('key columns do not match');
end

% accuracy
acc = sum(true_cls(:,2)==esom_cls(:,2))/size(true_cls,1);

% contingency table (using stats toolbox)
ct = crosstab(true_cls(:,2),esom_cls(:,2));

bye
fabian