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I have small dataset of 15 points. K-means clustering 2 time gives me this result.

Besides the random initializing the centroids, what could a reason for this bizarre graph(1st one) that it has given? I am yet to understand this simple algorithm to full extent.

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  • $\begingroup$ What's the difference between graph 1 and 2? Did you do anything different? Are you fitting 3 clusters? $\endgroup$
    – Matthew Gunn
    Commented Nov 4, 2016 at 20:41
  • $\begingroup$ How many iterations of the k-means clustering are you doing in each case? That could possibly have an effect (related to random initialization). $\endgroup$
    – MathIsKey
    Commented Nov 4, 2016 at 20:46
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    $\begingroup$ It got stuck in a local minimum. Here's a great run-down on the drawbacks of k-means clustering: stats.stackexchange.com/questions/133656/… $\endgroup$
    – Phil
    Commented Nov 4, 2016 at 20:54
  • $\begingroup$ @AWashburn I Iterated it 10 times over the same dataset I have $\endgroup$
    – linthum
    Commented Nov 4, 2016 at 23:52
  • $\begingroup$ @MatthewGunn : Yes I have fitting it with 3 clusters $\endgroup$
    – linthum
    Commented Nov 4, 2016 at 23:53

1 Answer 1

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If you choose the right-bottom-most two objects as starting points, the centers will remain stuck there, and never move to the top right.

The green points are closest to the green center, and will remain there.

This is a common problem with k-means because of random starting. A heuristic such as k-means++ is less likely to choose this starting situation.

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