Clustering - different size clusters
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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
2015 年 10 月 29 日
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
2015 年 11 月 6 日
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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