Matlab:K-means聚类

Question

我有一个A(369x10)的矩阵,我想在19个星团中聚类.我用这种方法

[idx ctrs]=kmeans(A,19)

产生idx(369x1)和ctrs(19x10)

我明白了这一点.A中的所有行都聚集在19个簇中.

现在我有一个数组B(49x10).我想知道这个B的行在给定的19个簇中对应的位置.

在MATLAB中怎么可能？

先感谢您

Answer 1

以下是有关群集的完整示例:

%% generate sample data
K = 3;
numObservarations = 100;
dimensions = 3;
data = rand([numObservarations dimensions]);

%% 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), 50, clustIDX, 'filled')
scatter3(clusters(:,1),clusters(:,2),clusters(:,3), 200, (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)

Answer 2

我想不出比你所描述的更好的方法了。内置函数可以保存一行，但我找不到。这是我要使用的代码：

[ids ctrs]=kmeans(A,19);
D = dist([testpoint;ctrs]); %testpoint is 1x10 and D will be 20x20
[distance testpointID] = min(D(1,2:end));