Clustering - different size clusters
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Bran
el 29 de Oct. de 2015
Comentada: Image Analyst
el 6 de Nov. de 2015
I have a pretty large matrix of data which I want to cluster against the first column which can be separated into six clusters / categories of different sizes. I know the k means clustering algorithm allows input of number of clusters but allows those to be determined iteratively. Is there anything on MATLAB which would be suitable for my task?
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Image Analyst
el 29 de Oct. de 2015
Yes, silhouette() lets you graphically judge the quality of the clustering produced by kmeans(). evalclusters() lets to evaluate the quality of the clustering achieved with a range of k values so you can pick the right k if you don't know it for certain.
% Try values of k 2 through 5
clustev = evalclusters(X, 'kmeans', 'silhouette', 'KList', 2:5);
% Get the best one value for k:
kBest = clustev.OptimalK
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Image Analyst
el 6 de Nov. de 2015
This is all I could find:
p = ranksum(x,y) returns the p-value of a two-sided Wilcoxon rank sum test. ranksum tests the null hypothesis that data in x and y are samples from continuous distributions with equal medians, against the alternative that they are not. The test assumes that the two samples are independent. x and y can have different lengths. This test is equivalent to a Mann-Whitney U-test.
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