"SOM (SOMDimensionality Reduction) documentation or examples"
User401
New Altair Community Member
Hi all,
I'm looking into the use of RMiner's SOM. I can get it to produce nice looking results but would like more information on its setup and interpreting the type of results in RMiner.
Does anyone have any worked examples or documentation? I'm wondering, in particular, how to interpret the resulting plot view and data view, relating the points back to the original data features.
I'd be very grateful for any help.
Thanks,
Richie
I'm looking into the use of RMiner's SOM. I can get it to produce nice looking results but would like more information on its setup and interpreting the type of results in RMiner.
Does anyone have any worked examples or documentation? I'm wondering, in particular, how to interpret the resulting plot view and data view, relating the points back to the original data features.
I'd be very grateful for any help.
Thanks,
Richie
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0
Answers
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Hi Richie,
I think I told you in a previous post that RM is much better than its documentation, this is a case in point. Try this code, and waft your mouse over the data points of your SOM plot ( coloured by label probably ), behold the Ids!<operator name="Root" class="Process" expanded="yes">
While the human eyeball mk.IV is about as smart as it gets in terms of finding and enjoying patterns, it sometimes may like patterns that are not helpful; in this latter case wheel out some horrible counting stuff to give the semblance of reason. In your context it is a matter of checking that the reduction produces "better" performance. So cross validate a learner on the original dataset, then reduce it and repeat the process. If it does, even after you've messed around with genetic optimisers etc.etc.., then you may have found an abstraction. ;D
<operator name="ExampleSetGenerator" class="ExampleSetGenerator">
<parameter key="target_function" value="random"/>
</operator>
<operator name="IOMultiplier" class="IOMultiplier">
<parameter key="io_object" value="ExampleSet"/>
</operator>
<operator name="IdTagging" class="IdTagging">
<parameter key="create_nominal_ids" value="true"/>
</operator>
<operator name="SOMDimensionalityReduction" class="SOMDimensionalityReduction">
</operator>
</operator>
I stress the phrase "semblance of reason", because no matter what you do you will be left with doubt, known here as the "Curse of MIerswa" - did you realise that "Ingo Mierswa" can be rearranged as "imagine rows"?
Augustine of Hippodubito ergo sum ("I doubt, therefore I exist") and si fallor sum ("If I am deceived, I exist")
Ergo clearly a dataminer.0 -
Amazing! I tried so many years to keep my true nature hidden as deep as possible. Like other suspicous creatures I of course was not able to bear this burden all alone and started to give hints to others. I have to admit that writing an application (RapidMiner) to give others a hint to the true purpose of myself was maybe too strong a hint but nevertheless: after only eight years of development, finally somebody (you) managed to break this secret and unveil my secret nature.
I stress the phrase "semblance of reason", because no matter what you do you will be left with doubt, known here as the "Curse of MIerswa" - did you realise that "Ingo Mierswa" can be rearranged as "imagine rows"?
Of course, I am sure that you noticed that "Ingo Mierswa" is also an anagram of "Image In Rows" (Image, Pattern, all the same) and of course "Mirage Is Now" which I personally like most
By the way: thanks again for your valuable posts which I enjoy not only reading because of their quality in terms of the topic but also because of the quality of language. You really should consider to write a true "Data Mining Novelette". I would certainly buy it!
Cheers,
Ingo0 -
By the way: if you rearrange "RapidMiner" you will end up with "Repair Mind". More thorough investigation might be necessary here.
Cheers,
Ingo0 -
Excellent
Bravo, bravo ;D ;D ;D0 -
I totally agreee ! I also enjoy reading the valuable suggestions and the fine humour of Mr HaddockIngo Mierswa wrote:
By the way: thanks again for your valuable posts which I enjoy not only reading because of their quality in terms of the topic but also because of the quality of language. You really should consider to write a true "Data Mining Novelette". I would certainly buy it!0