MATLAB-分类输出 [英] MATLAB - Classification output

问题描述

我的程序使用K-means群集，该群集来自用户一定数量的群集.对于这个k = 4，但是我想稍后通过matlabs天真的贝叶斯分类器运行聚类信息.

My programme uses K-means clustering of a set amount of clusters from the user. For this k=4 but I would like to run the clustered information through matlabs naive bayes classifier afterwards.

是否有一种方法可以将集群拆分并馈入Matlab中的不同朴素分类器?

Is there a way to split the clusters up and feed them into different naive classifiers in matlab?

朴素贝叶斯:

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

K均值:

    %% 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)

类似于将k个聚类输出为k1，k2，k3格式，然后让朴素的分类器选择它们，而不是测试它就是k1，k2.等等.

something like outputing k clusters to a format k1,k2,k3 then having the naive classifier pick those up, instead of test it would be k1,k2.. etc

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

但是我只是不知道如何将matlab中的k个簇的输出发送到某种类型的格式? (这是该计划的新手)

But I just dont know how to send the output of the k clusters in matlab to some type of format? (really new to this programme)

编辑

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

推荐答案

尝试一下:

% 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);

如果只想从数据中筛选出一个集群，则可以执行以下操作:

And if you just want to filter out one of the clusters out of the data you can do something like that:

K1 = X(IDX==1)

希望是有帮助的..

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