It reinforces the influence of longer sequences. Element j1 is the leftmost element of the right subtree of this node(i,j).Ī numerical or character vector, containing the class labels of the dataset objects.Ī parameter used in the expression for the calculation of the evaluation function. Each cell (i,j) of the matrix maxJ stores the code of the intermediate element j1, that provides the best value A(i,j) of the evaluation function for the subtree or node(i,j), which has element i as the leftmost and element j as the rightmost. Element i1 is the rightmost element of the left subtree of this node(i,j).Ī nxn numeric matrix. Each cell (i,j) of the matrix maxI stores the code of the intermediate element i1, that provides the best value A(i,j) of the evaluation function for the subtree or node(i,j), which has element i as the leftmost and element j as the rightmost. Each cell (i,j) of the matrix A stores the best ordering (according to the evaluation function value) of the subtree starting with element i and ending with element j.Ī numerical nxn matrix, each cell stores the number of the elements with the same class label, starting from the leftmost or the rightmost element of the subtree.Ī nxn numeric matrix. The value of the last row of the merge matrix, which is equal to n-1, where n is the number of data objects.Īn object of class hclust which describes the tree produced by the clustering process.There are such components: merge, height, order, labels,call,method,thod.Ī list of lists of length n-1 testBar, where each single list stores two vectors, consisting of elements of the left and right subtrees of the corresponding node in the merging matrix hcmatrĪ numerical nxn matrix, which stores the values of the evaluation function fir the subtrees. OrderingJoseph(ind, hc, node, A, r, maxI, maxJ, class, coef) The cells of the A matrix store the values of the evaluation function for each subtree of the hierarchical tree. As an output the four auxiliary matrices A, R, maxI, maxJ are returned to the RearrangeJoseph function. The optimal reordering is made according to the available class labels. The function realizes the dynamic programming approach in order to find the optimal reordering of the leaves of the hierarchical tree. Makes the calculation of the evaluation function for each subtree of the hierarchical tree using the dynamic programming approach testData2: Simulates the dataset for analysis.testData1: Simulates the dataset for analysis.testBar: For each node (subtree) of the hierarchical tree forms two.SubTree: Simplifies the initial hierarchical tree by reducing the.ReorderCluster-package: optimal reordering of the hierarchical tree according to.RearrangeJoseph: Makes the initialization of auxiliary matrices and calls to.RearrangeData: Sample function to perform optimal reordering of the.OrderingJosephC: Makes the calculation of the evaluation function for each.OrderingJoseph: Makes the calculation of the evaluation function for each.leukemia72: Real biological dataset to perform the analysis.leukemia: Real biological dataset to perform the analysis.funMerge: Recover the optimal sequence of leaves in the hierarchical.colorDendClass: Makes the plot of the dendrogram, visualizing the class label.CalcMerge: Forms the binary vector to mark the nodes with identical.
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