在群集的kmeans数据上显示行 [英] Show rows on clustered kmeans data

问题描述

我想知道当您在图形屏幕上对数据进行聚类时，是否有一种方法可以显示在滚动数据点时数据点属于哪些行?

Hi I was wondering when you cluster data on the figure screen is there a way to show which rows the data points belong to when you scroll over them?

从上面的图片中，我希望有一种方法，如果我选择或滚动这些点，就可以知道它属于哪一行.

From the picture above I was hoping there would be a way in which if I select or scroll over the points that I could tell which row it belonged to.

这是代码:

%% dimensionality reduction 
columns = 6
[U,S,V]=svds(fulldata,columns);
%% randomly select dataset
rows = 1000;
columns = 6;

%# pick random rows
indX = randperm( size(fulldata,1) );
indX = indX(1:rows);

%# pick random columns
indY = randperm( size(fulldata,2) );
indY = indY(1:columns);

%# filter data
data = U(indX,indY);
%% apply normalization method to every cell
data = data./repmat(sqrt(sum(data.^2)),size(data,1),1);

%% generate sample data
K = 6;
numObservarations = 1000;
dimensions = 6;

%% cluster
opts = statset('MaxIter', 100, '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)

或者可能是集群数据的一种输出方法，将其规范化并重新组织成原始格式，在原始列的末尾列上带有小节，它属于来自原始"fulldata"的行.

Or possibly an output method of the clusters data, normalized and re-organized to there original format with appedicies on the end column with which row it belonged to from the original "fulldata".

推荐答案

您可以使用

You could use the data cursors feature which displays a tooltip when you select a point from the plot. You can use a modified update function to display all sorts of information about the point selected.

这是一个有效的示例:

function customCusrorModeDemo()
    %# data
    D = load('fisheriris');
    data = D.meas;
    [clustIdx,labels] = grp2idx(D.species);
    K = numel(labels);
    clr = hsv(K);

    %# instance indices grouped according to class
    ind = accumarray(clustIdx, 1:size(data,1), [K 1], @(x){x});

    %# plot
    %#gscatter(data(:,1), data(:,2), clustIdx, clr)
    hLine = zeros(K,1);
    for k=1:K
        hLine(k) = line(data(ind{k},1), data(ind{k},2), data(ind{k},3), ...
            'LineStyle','none', 'Color',clr(k,:), ...
            'Marker','.', 'MarkerSize',15);
    end
    xlabel('SL'), ylabel('SW'), zlabel('PL')
    legend(hLine, labels)
    view(3), box on, grid on

    %# data cursor
    hDCM = datacursormode(gcf);
    set(hDCM, 'UpdateFcn',@updateFcn, 'DisplayStyle','window')
    set(hDCM, 'Enable','on')

    %# callback function
    function txt = updateFcn(~,evt)
        hObj = get(evt,'Target');   %# line object handle
        idx = get(evt,'DataIndex'); %# index of nearest point

        %# class index of data point
        cIdx = find(hLine==hObj, 1, 'first');

        %# instance index (index into the entire data matrix)
        idx = ind{cIdx}(idx);

        %# output text
        txt = {
            sprintf('SL: %g', data(idx,1)) ;
            sprintf('SW: %g', data(idx,2)) ;
            sprintf('PL: %g', data(idx,3)) ;
            sprintf('PW: %g', data(idx,4)) ;
            sprintf('Index: %d', idx) ;
            sprintf('Class: %s', labels{clustIdx(idx)}) ;
        };
    end

end

这是2D和3D视图(显示样式不同)中的外观:

Here is how it looks like in both 2D and 3D views (with different display styles):

这篇关于在群集的kmeans数据上显示行的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！