Show rows on clustered kmeans data

问题:

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?

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.

Here is the code:

%% 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) 

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".

回答1:

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.

Here is a working example:

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 

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

文章来源: Show rows on clustered kmeans data