Reorder rows of a matrix by hierarchical average linkage clustering on Euclidean distances

clusterReorderRows.m

function [dataMatrixClustered,dataMatrixNorm] = clusterReorderRows(dataMatrix)
    distanceMetric = 'Euclidean';
    linkageMethod = 'average';

    % Normalize columns:
    dataMatrixNorm = zscore(dataMatrix);

    % Pairwise distances:
    R = pdist(dataMatrix,distanceMetric);

    % Do hierarchical linkage:
    links = linkage(R,linkageMethod);
    f = figure('color','w');
    set(gcf,'Visible','off'); % suppress figure output
    [~,~,ord] = dendrogram(links,0);
    close(f); % close the invisible figure used for the dendrogram

    % Reorder rows:
    dataMatrixClustered = dataMatrixNorm(ord,:);

    % Plot:
    figure('color','w');
    subplot(1,2,1);
    imagesc(dataMatrixNorm);
    subplot(1,2,2);
    imagesc(dataMatrixClustered);
    
end

Note that the column normalization step is something you should think about for your application (also whether it makes sense to normalize along rows) [and also whether a normalization other than z-score is more suitable]