# kmeans clustering of matrices

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Susan 2021 年 6 月 4 日
コメント済み: Susan 2021 年 6 月 7 日
Hi All,
I have 12X190 cells. Each cell contains a complex matrix of size n*550 (assuming each row is an observation on 550 variables. The number of observations varies cell to cell but the variables are the same for each matrix). I need to classify these matrices using kmeans and I am trying to cluster the large matrix (i.e., 12*190*n*550 and I am not working with each matrix separately).
Any idea how I can do that? Any method better than kmeans to cluster these data? Any input would be appreciated.
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Image Analyst 2021 年 6 月 5 日
OK, so you're just going to consider the real part of the complex numbers. So, how many clusters do you believe there to be? What did you put in for k (if you put in anything)? Do you think there are 3 clusters? 6? 100? Or no idea?
Susan 2021 年 6 月 5 日
@Image Analyst There would be 19 cluster

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### 採用された回答

Walter Roberson 2021 年 6 月 5 日
k-means is not the right technology for situations in which you have labels, except for the situation in which the labels have numeric values that can be made commensurate with the numberic coordinates. For example if you can say that having a label differ by no more than 1 is 10.28 times as important as having column 3 differ by 1, then you might be able to use k-means by adding the numeric value of the label as an additional coordinate. But this is not the usual case.
When you have matrices of numbers and a label associated with the matrix, then Deep Learning or (Shallow) Neural Network techniques are more appropriate. Consider that if you have a matrix of data and a label, and the matrices are all the same size, that that situation could be treated the same was as if the matrix of data were an "image"
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Walter Roberson 2021 年 6 月 7 日
Yes! This is expected, and is a fundamental challenge of this kind of learning: to determine the best subset of data to train on for the highest accuracy and lowest over-training.
k-fold cross validation is indeed one of the techniques that is used. It will reduce the variation you see, but do expect that there will still be some variation depending on the random choice.
Susan 2021 年 6 月 7 日
Thanks!

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