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Octave clustering demo part 4: k-Medoids

[This post is part of the Octave clustering demo series]

Here follows the description of the first of two new Octave clustering demos. If you have attended the PAMI class this year you probably know what I am talking about. If instead you are new to these demos please (1) check the page linked above and (2) set up your Octave environment (feel free to download the old package and play with the old demos too ;-)). Then download this new package.

Run the kMedoidsDemo script (if launched without input parameters, it will open the "noisyBlobs01.mat" data file by default). The script will plot the dataset, then it will try to perform clustering using first k-means and then k-medoids. Run the experiment many times (at least 4-5 times) for each of the three noisyBlobs datasets (you can pass the dataset path and name as a parameter to the kMedoidsDemo function).


  1. comment the "worst case" results you get from k-means: what happens and how does noise influence this behavior?
  2. compare the results you get with k-means with the ones you get with k-medoids: how does k-medoids deal with the presence of noise?
  3. what would happen if noise points were much farther from the original clusters? Try to get few (1-2) of them and bring them very far from the rest of the data… what are the results you get from k-means and k-medoids?


  • the demo stops at each step waiting for you to press a key. You can disable this feature by setting the "interactive" variable to zero at the beginning of the script;
  • there are two different k-means implementations you can run: feel free to try both just by uncommenting their respective code.
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