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Author Topic: [SOLVED] KMeans and Nominal Measures  (Read 4325 times)
annbra
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« on: May 08, 2013, 07:33:04 AM »

Hi,

I am new in the forum and I have a question about kMeans.
I have read in the forum that it is not possible to use polynominal data for kMeans algorithm. But in RapidMiner 5.2.008, it is possible to choose nominal measures (e.g. nominal distance) and to determine clusters with centroids of polynominal data. There is only a warning that it is not possible to use polynominal data by kMeans, but it is possible because I get "good" results.
How does RapidMiner calculate the clusters? Is it possible to have a look on the different steps during the calculation?

Greetings,
Anne
« Last Edit: May 24, 2013, 10:30:37 AM by Marius » Logged
Marius
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« Reply #1 on: May 08, 2013, 01:03:27 PM »

Hi Anne,

it is not possible to see intermediate steps of the algorithm. But yes, k-Means is capable of handling nominal attributes with the nominal or mixed measures. NominalDistance e.g. is 0 if two strings match exactly, and 1 otherwise. The issue about the warning (which is obviously wrong) has just been fixed and will disappear in the next release.

Best regards,
Marius
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annbra
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« Reply #2 on: May 09, 2013, 09:25:15 AM »

Hi Marius,

thank you very much for your answer.
The calculation of distance is now clear but how does the algorithm determine the centroid. A centroid of one cluster is a linear combination of the examples, and for this you need numeric data. Does the algorithm transfer the polynominal data in natural numbers and determine the centroid by "normal" summation?

Greetings,
Anne
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Marius
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« Reply #3 on: May 09, 2013, 04:15:55 PM »

Actually it seems so. It may be better to use Nominal to Numerical with coding_type=dummy_coding before actually applying k-Means. That way you'll be on the safe side.
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annbra
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« Reply #4 on: May 16, 2013, 10:40:38 AM »

Thank you very much.
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