scipy.cluster.hierarchy.linkage¶
- scipy.cluster.hierarchy.linkage(y, method='single', metric='euclidean')[source]¶
Performs hierarchical/agglomerative clustering on the condensed distance matrix y.
y must be a sized vector where n is the number of original observations paired in the distance matrix. The behavior of this function is very similar to the MATLAB linkage function.
A 4 by matrix Z is returned. At the -th iteration, clusters with indices Z[i, 0] and Z[i, 1] are combined to form cluster . A cluster with an index less than corresponds to one of the original observations. The distance between clusters Z[i, 0] and Z[i, 1] is given by Z[i, 2]. The fourth value Z[i, 3] represents the number of original observations in the newly formed cluster.
The following linkage methods are used to compute the distance between two clusters and . The algorithm begins with a forest of clusters that have yet to be used in the hierarchy being formed. When two clusters and from this forest are combined into a single cluster , and are removed from the forest, and is added to the forest. When only one cluster remains in the forest, the algorithm stops, and this cluster becomes the root.
A distance matrix is maintained at each iteration. The d[i,j] entry corresponds to the distance between cluster and in the original forest.
At each iteration, the algorithm must update the distance matrix to reflect the distance of the newly formed cluster u with the remaining clusters in the forest.
Suppose there are original observations in cluster and original objects in cluster . Recall and are combined to form cluster . Let be any remaining cluster in the forest that is not .
The following are methods for calculating the distance between the newly formed cluster and each .
method=’single’ assigns
for all points in cluster and in cluster . This is also known as the Nearest Point Algorithm.
method=’complete’ assigns
for all points in cluster u and in cluster . This is also known by the Farthest Point Algorithm or Voor Hees Algorithm.
method=’average’ assigns
for all points and where and are the cardinalities of clusters and , respectively. This is also called the UPGMA algorithm. This is called UPGMA.
method=’weighted’ assigns
where cluster u was formed with cluster s and t and v is a remaining cluster in the forest. (also called WPGMA)
method=’centroid’ assigns
where and are the centroids of clusters and , respectively. When two clusters and are combined into a new cluster , the new centroid is computed over all the original objects in clusters and . The distance then becomes the Euclidean distance between the centroid of and the centroid of a remaining cluster in the forest. This is also known as the UPGMC algorithm.
method=’median’ assigns math:d(s,t) like the centroid method. When two clusters and are combined into a new cluster , the average of centroids s and t give the new centroid . This is also known as the WPGMC algorithm.
method=’ward’ uses the Ward variance minimization algorithm. The new entry is computed as follows,
where is the newly joined cluster consisting of clusters and , is an unused cluster in the forest, , and is the cardinality of its argument. This is also known as the incremental algorithm.
Warning: When the minimum distance pair in the forest is chosen, there may be two or more pairs with the same minimum distance. This implementation may chose a different minimum than the MATLAB version.
Parameters: y : 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 observation vectors in n dimensions may be passed as an by array.
method : str, optional
The linkage algorithm to use. See the Linkage Methods section below for full descriptions.
metric : str, optional
The distance metric to use. See the distance.pdist function for a list of valid distance metrics.
Returns: Z : ndarray
The hierarchical clustering encoded as a linkage matrix.