r_cluster_draft.m

% Cluster_data
clear,

% add data folder to path
addpath('data')

% load psy measures
load psymeasdata.mat

% ------
% var psymeasname - name of the variables available 
% var npsymeas - values of the variables
% ------

%% presets
cluster_num = 3;


%% data preprocessing

% data de-mean and data normalization
data = (npsymeas - min(npsymeas)) ./ ( max(npsymeas) - min(npsymeas) );

% find clusters in data
[c_idx,c_means] = kmeans(data,...
    cluster_num,... % number of clusters
    'replicates',5,... % repeats the clustering process starting from different randomly selected centroids for each replicate
    'display', 'iter',... 
    'dist', 'sqeuclidean'); % use euclidean distance to determine best centroid for each point

% display silhouette - value corresponds to fit of each point and distance
% to other class
figure
[silh, ~] = silhouette(data,...
    c_idx,...
    'sqeuclidean');


% disply a 3D plot with cluster
figure
ptsymb = {'bs','r^','md','go','c+'};

for i = 1:cluster_num
    clust = find(c_idx == i);
    plot3(data(clust,1),data(clust,2),data(clust,3),ptsymb{i});
    hold on
end

plot3(c_means(:,1),c_means(:,2),c_means(:,3),'ko');
plot3(c_means(:,1),c_means(:,2),c_means(:,3),'kx');
hold off

view(-137,10);
grid on

%%
eucD = pdist(data,'euclidean');
clustTreeEuc = linkage(eucD,'average');

[h,nodes] = dendrogram(clustTreeEuc,0);
h_gca = gca;
h_gca.TickDir = 'out';
h_gca.TickLength = [.002 0];