# K means for multidimensional data

43 views (last 30 days)
ananya mittal on 13 Jun 2020
Commented: Image Analyst on 15 Jun 2020
Hi everyone. I am trying to perform Raman spectral analysis using K-means clustering . I have 100 spectrums over 534 variables(in a matrix of 100 x 534).
Now I want to cluster 100 objects .How can I do so?
I am trying with this code, K= 12 found out by iteration. Now I have to find a plot of this for my data . Please help .
K=[ ];
sa=[ ];
for k=1:20
[idx c sumd]= kmeans(matrix,k);
sa= [sa sum(sumd)];
K= [K k];
end
plot(K,sa);// to find appropriate k
idx = kmeans(matrix,12);
gscatter(scoress(:,1),scoress(:,2),scoress(:,3),idx);//
now here I need to plot the data for all the columns rather than just 2 columns. How can I do so?
Image Analyst on 13 Jun 2020
Edited: Image Analyst on 13 Jun 2020
So you have 100 observations for each absorbance (wavenumber). The absorbance at each wavenumber are the features. And now you want 12 clusters which will classify each spectrum into one of 12 possible classes? Can you attach your matrix so we can try it?

Image Analyst on 14 Jun 2020
Well this is what I got so far
clc; % Clear the command window.
fprintf('Beginning to run %s.m ...\n', mfilename);
close all; % Close all figures (except those of imtool.)
clear; % Erase all existing variables. Or clearvars if you want.
workspace; % Make sure the workspace panel is showing.
format short g;
format compact;
fontSize = 15;
[numbers, strings, raw] = xlsread('data matrix.xlsx');
[rows, columns] = size(numbers)
wavenumbers = numbers(:, 1);
for col = 2 : columns
thisSpectrum = numbers(:, col);
plot(wavenumbers, thisSpectrum, '-');
grid on;
hold on;
end
title('All Raman Spectra', 'FontSize', 20);
xlabel('Wavenumber', 'FontSize', 20);
ylabel('Absorbance', 'FontSize', 20);
[classNumber, classCentroid] = kmeans(numbers(:, 2:end)', 12)
% Plot each clas separately
hFig = figure;
for col = 2 : columns
thisClass = classNumber(col - 1);
thisSpectrum = numbers(:, col);
subplot(3, 4, thisClass);
plot(wavenumbers, thisSpectrum, '-');
grid on;
hold on;
caption = sprintf('Class #%d', thisClass);
title(caption, 'FontSize', 20, 'Interpreter', 'none');
xlabel('Wavenumber', 'FontSize', 20);
ylabel('Absorbance', 'FontSize', 20);
end
hFig.WindowState = 'maximized'
but I'm not really sure kmeans is what you want to do, as you can see from the spectra plotted for each class. I might talk to my spectroscopists tomorrow and see if they have any ideas. They are really world class. What do you want me to ask him or her?
Image Analyst on 15 Jun 2020
I don't know what the elbow method is. But there are ways to have kmeans decide what the best value of k is. Some other function I think - I don't remember what it is off the top of my head. Maybe it's 12 but maybe it's not.

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