MATLAB - Sortie de classification

Question

Mon programme utilise le clustering K-means d'un nombre défini de clusters de l'utilisateur. Pour ce k = 4, mais je voudrais exécuter les informations en cluster par l'intermédiaire de matlabs naïf bayes classificateur après.

Existe-t-il un moyen de diviser les clusters et de les alimenter dans différents classificateurs naïfs dans matlab?

Naive Bayes:

class = classify(test,training, target_class, 'diaglinear'); 

K-means:

%% generate sample data 
K = 4; 
numObservarations = 5000; 
dimensions = 42; 
%% cluster 
opts = statset('MaxIter', 500, 'Display', 'iter'); 
[clustIDX, clusters, interClustSum, Dist] = kmeans(data, K, 'options',opts, ... 
'distance','sqEuclidean', 'EmptyAction','singleton', 'replicates',3); 
%% plot data+clusters 
figure, hold on 
scatter3(data(:,1),data(:,2),data(:,3), 5, clustIDX, 'filled') 
scatter3(clusters(:,1),clusters(:,2),clusters(:,3), 100, (1:K)', 'filled') 
hold off, xlabel('x'), ylabel('y'), zlabel('z') 
%% plot clusters quality 
figure 
[silh,h] = silhouette(data, clustIDX); 
avrgScore = mean(silh); 
%% Assign data to clusters 
% calculate distance (squared) of all instances to each cluster centroid 
D = zeros(numObservarations, K);  % init distances 
for k=1:K 
%d = sum((x-y).^2).^0.5 
D(:,k) = sum(((data - repmat(clusters(k,:),numObservarations,1)).^2), 2); 
end 
% find for all instances the cluster closet to it 
[minDists, clusterIndices] = min(D, [], 2); 
% compare it with what you expect it to be 
sum(clusterIndices == clustIDX) 

quelque chose comme outputing k grappes à un format k1, k2, k3 alors avoir le classificateur naïf choisir ceux qui, au lieu de tester ce serait k1, k2 .. etc

class = classify(k1,training, target_class, 'diaglinear'); 

Mais je ne sais tout simplement pas comment envoyer la sortie des k clusters en m Atlab à un certain type de format? (Vraiment nouveau à ce programme)

EDIT

training = [1;0;-1;-2;4;0]; % this is the sample data. 
target_class = ['posi';'zero';'negi';'negi';'posi';'zero'];% This should have the same number of rows as training data. The elements and the class on the same row should correspond. 
% target_class are the different target classes for the training data; here 'positive' and 'negetive' are the two classes for the given training data 

% Training and Testing the classifier (between positive and negative) 
test = 10*randn(10,1) % this is for testing. I am generating random numbers. 
class = classify(test,training, target_class, 'diaglinear') % This command classifies the test data depening on the given training data using a Naive Bayes classifier 

% diaglinear is for naive bayes classifier; there is also diagquadratic 

Answer 1

Essayez ceci:

% create 100 random points (this is the training data) 
X = rand(100,3); 

% cluster into 5 clusters 
K = 5; 
[IDX, C] = kmeans(X, K); 

% now let us say you have new data and you want 
% to classify it based on the training: 
SAMPLE = rand(10,3); 
CLASS = classify(SAMPLE,X,IDX); 

Et si vous voulez juste pour filtrer l'un des groupes de données que vous pouvez faire quelque chose comme ça:

K1 = X(IDX==1) 

Espérons que cela a été utile ..