MATLAB: I have this scrip, can somebody explain to me about the last line

clusteringkmeansStatistics and Machine Learning Toolbox

[m D.^0.5]

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m indicate the cluster index number corresponding to rows in data. D represent the distance of data points from cluster Centroid. D.^0.5 is used because by default kmeans() returns square of the Euclidean distance, so to convert it to normal distance, the square root is needed. In the end, we are concatenating both vectors. So you can interpret the result like this

[1  0.5  % first row of variable data belong to cluster 1 and its distance from centrioid of cluster 1 is 0.5
 2 0.7   % first row of variable data belong to cluster 2 and its distance from centrioid of cluster 1 is 0.7
 1 0.2   % similar intrepretation for all rows
 3 1
...
...]

Related Solutions

MATLAB: I need to know each point to which cluster in kmeans fun.

Huda, you gave kmeans two points and asked it to cluster them into two clusters. It has assigned the first point to cluster 1, whose centroid is at the first point, and similarly for the second point. Presumably that is not very informative.

I don't know what your data mean, or whether kmeans makes sense, but your description sounds like something more suited to distance-based methods such as hierarchical clustering or multidimensional scaling, both of which are available in the Statistics Toolbox. You would, of course, have to convert your similarities to dissimilarities.

MATLAB: What does sumd method in k-means clustering function exactly calculate

If you look at the documentation of kmeans(), you will know that it uses the square of the Euclidean distance, by default. So you should calculate it like this

abs(1-1.5).^2+abs(2-1.5).^2 or  abs(3-3.5).^2+abs(4-3.5).^2
ans = 
  0.5 (both cases)

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Related Solutions

MATLAB: I need to know each point to which cluster in kmeans fun.

MATLAB: What does sumd method in k-means clustering function exactly calculate

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