k-means clustering algorithm

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zhala sarkawt 2016-5-22

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评论： Walter Roberson 2017-12-28

For the data set shown below, execute the k-means clustering algorithm with k=2 till convergence. You should declare convergence when the cluster assignments for the examples no longer change. As initial values, set µ1 and µ2 equal to x(1) and x(3) respectively. Show your calculations for every iteration. x1 x2 1 1 1,5 2 2 1 2 0,5 4 3 5 4 6 3 6 4

1. You should start your calculation first by initializing your µ1 and µ2 as shown below. µ1 = x(1) =(1,1) µ2 = x(3) =(2,1) 2. For every iteration till convergence find c(i) for i = {1,2,3,4,5,6,7,8} then compute the average for each cluster and reassign the µ1 and µ2 3. Repeat 2 till convergence

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Image Analyst 2016-5-23

Thanks for the correction - apparently I overlooked it.

Walter Roberson 2017-12-28

http://www.mathworks.co.uk/matlabcentral/answers/43073-a-guide-to-tags

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Image Analyst 2016-5-23

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在 MATLAB Online 中打开

Hint:

x1x2 = [...
  1  1
  1.5  2
  2  1
  2  0.5
  4  3
  5  4
  6  3
  6  4]
x1 = x1x2(:, 1);
x2 = x1x2(:, 2);
mu1 = [1,1];
mu2 = [2,1];
for k = 1 : 4
  indexes = kmeans(x1x2, 2, 'start', [mu1;mu2])
  mu1 = mean(x1x2(indexes == 1, :), 1)
  mu2 = mean(x1x2(indexes == 2, :), 1)
end