# kmeans clustering of matrices

6 visualizaciones (últimos 30 días)
Susan el 4 de Jun. de 2021
Comentada: Susan el 7 de Jun. de 2021
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.
##### 11 comentariosMostrar 9 comentarios más antiguosOcultar 9 comentarios más antiguos
Image Analyst el 5 de Jun. de 2021
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 el 5 de Jun. de 2021
@Image Analyst There would be 19 cluster

Iniciar sesión para comentar.

Walter Roberson el 5 de Jun. de 2021
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"
##### 5 comentariosMostrar 3 comentarios más antiguosOcultar 3 comentarios más antiguos
Walter Roberson el 7 de Jun. de 2021
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 el 7 de Jun. de 2021
Thanks!

Iniciar sesión para comentar.

### Categorías

Más información sobre k-Means and k-Medoids Clustering en Help Center y File Exchange.

### Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by