MATLAB: K-means clustering, error

clusteringk-meansMATLAB

Hello All, I really need your help, your advice. I am trying to do clustering, I have mixed data and big dataset. From all datased I selected only some of them. I do k-means algorithm but error is shown on my command window. Could you advise me, where is bug?

Thank you very very much…

Best Answer

What data type are you entering as the first argument to the kmeans command? According to the documentation, it has to be an n-by-p data matrix, but it looks like you have input a dataset instead.

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MATLAB: How to cluster a dataset having a vector of clustered indeces

Your data is a matrix of size [10 x 9].

kmeans() identifies the cluster of each row of the matrix so its output will be a vector whose length is equal to the number of rows of your matrix (10 rows).

Your i-loop loops through each row (1:10). But your indexing your data by column: data(:,1). You only have 9 columns so on the last iteration, there's an error.

I think what you meant to write is:

clustered = [clustered; (data(i,:))];  
%                     ^       ^         Note the restructuring.

Now your code works for the [10 x 9] inputs.

MATLAB: Is there any matlab function for K-means++ with handling missing data

WANTING to cluster your data is fine, but nothing says that you can always get what you want. We all want lots of things, many of which may be impossible to achieve. The clustering tools in MATLAB do not allow for missing data. Sorry. You could try to write your own tool that does do so. Or you might try to find one. I don't know of any.

So just exclude any data points that are missing, and cluster those that remain.

I might point out the help for kmeans (at least in the current version) says:

"kmeans treats NaNs as missing data, and ignores any rows of X that contain NaNs."

Since you are using an older release, I cannot know if that capability is in your release or not. Regardless, it is trivial to drop those points, which is all that kmeans would do anyway. There simply is no good way to cluster a point that has missing values.

Best Answer

Related Solutions

MATLAB: How to cluster a dataset having a vector of clustered indeces

MATLAB: Is there any matlab function for K-means++ with handling missing data

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