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The function used to determine the distance between two clusters, known as the linkage function, is what differentiates the agglomerative clustering methods. In single-linkage clustering, the distance between two clusters is determined by a single pair of elements: those two elements (one in each cluster) that are closest to each other.
Alternative linkage schemes include single linkage clustering and average linkage clustering - implementing a different linkage in the naive algorithm is simply a matter of using a different formula to calculate inter-cluster distances in the initial computation of the proximity matrix and in step 4 of the above algorithm. An optimally ...
As with complete linkage and average distance, the difficulty of calculating cluster distances causes the nearest-neighbor chain algorithm to take time and space O(n 2) to compute the single-linkage clustering. However, the single-linkage clustering can be found more efficiently by an alternative algorithm that computes the minimum spanning ...
Several standard clustering algorithms such as single linkage, complete linkage, and group average method have a recursive formula of the above type. A table of parameters for standard methods is given by several authors. [2] [3] [4] Ward's minimum variance method can be implemented by the Lance–Williams formula.
The standard algorithm for hierarchical agglomerative clustering (HAC) has a time complexity of () and requires () memory, which makes it too slow for even medium data sets. . However, for some special cases, optimal efficient agglomerative methods (of complexity ()) are known: SLINK [2] for single-linkage and CLINK [3] for complete-linkage clusteri
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some specific sense defined by the analyst) to each other than to those in other groups (clusters). It is a main task of exploratory data analysis, and a common technique for statistical ...
Alternative linkage schemes include single linkage clustering, complete linkage clustering, and WPGMA average linkage clustering. Implementing a different linkage is simply a matter of using a different formula to calculate inter-cluster distances during the distance matrix update steps of the above algorithm.
Network science. The Louvain method for community detection is a method to extract non-overlapping communities from large networks created by Blondel et al. [1] from the University of Louvain (the source of this method's name). The method is a greedy optimization method that appears to run in time where is the number of nodes in the network.