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SciPy Reference Guide, Release 0.8.dev average(y) Performs average/UPGMA linkage on the condensed distance matrix y. See linkage for more information on the return structure and algorithm. Parameters y Returns Z Seealso [ndarray] The upper triangular of the distance matrix. The result of pdist is returned in this form. [ndarray] A linkage matrix containing the hierarchical clustering. See the linkage function documentation for more information on its structure. • linkage: for advanced creation of hierarchical clusterings. centroid(y) Performs centroid/UPGMC linkage. See linkage for more information on the return structure and algorithm. The following are common calling conventions: 1.Z = centroid(y) Performs centroid/UPGMC linkage on the condensed distance matrix y. See linkage for more information on the return structure and algorithm. 2.Z = centroid(X) Performs centroid/UPGMC linkage on the observation matrix X using Euclidean distance as the distance metric. See linkage for more information on the return structure and algorithm. Parameters Q Returns Z Seealso [ndarray] A condensed or redundant distance matrix. A condensed distance matrix is a flat array containing the upper triangular of the distance matrix. This is the form that pdist returns. Alternatively, a collection of m observation vectors in n dimensions may be passed as a m by n array. [ndarray] A linkage matrix containing the hierarchical clustering. See the linkage function documentation for more information on its structure. • linkage: for advanced creation of hierarchical clusterings. complete(y) Performs complete complete/max/farthest point linkage on the condensed distance matrix y. See linkage for more information on the return structure and algorithm. 132 Chapter 3. Reference
Parameters y Returns Z SciPy Reference Guide, Release 0.8.dev [ndarray] The upper triangular of the distance matrix. The result of pdist is returned in this form. [ndarray] A linkage matrix containing the hierarchical clustering. See the linkage function documentation for more information on its structure. cophenet(Z, Y=None) Calculates the cophenetic distances between each observation in the hierarchical clustering defined by the linkage Z. Suppose p and q are original observations in disjoint clusters s and t, respectively and s and t are joined by a direct parent cluster u. The cophenetic distance between observations i and j is simply the distance between clusters s and t. Parameters • Z : ndarray The hierarchical clustering encoded as an array (see linkage function). • Y : ndarray (optional) Calculates the cophenetic correlation coefficient c of a hierarchical clustering defined by the linkage matrix Z of a set of n observations in m dimensions. Y is the condensed distance matrix from which Z was generated. Returns (c, {d}) - c : ndarray The cophentic correlation distance (if y is passed). • d : ndarray The cophenetic distance matrix in condensed form. The ij th entry is the cophenetic distance between original observations i and j. correspond(Z, Y) Checks if a linkage matrix Z and condensed distance matrix Y could possibly correspond to one another. They must have the same number of original observations for the check to succeed. This function is useful as a sanity check in algorithms that make extensive use of linkage and distance matrices that must correspond to the same set of original observations. Arguments Returns • Z [ndarray] The linkage matrix to check for correspondance. • Y [ndarray] The condensed distance matrix to check for correspondance. • b [bool] A boolean indicating whether the linkage matrix and distance matrix could possibly correspond to one another. 3.1. Clustering package (<strong>scipy</strong>.cluster) 133
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