Nearest-neighbor chain algorithm: Difference between revisions

Revision as of 08:23, 11 December 2016

Revision as of 08:24, 11 December 2016

Line 3:

The main idea of the algorithm is to find pairs of clusters to merge by following [[Path (graph theory)|paths]] in the [[nearest neighbor graph]] of the clusters. Every such path will eventually terminate at a pair of clusters that are nearest neighbors of each other, and the algorithm chooses that pair of clusters as the pair to merge. In order to save work by re-using as much as possible of each path, the algorithm uses a [[Stack (abstract data type)|stack data structure]] to keep track of each path that it follows. By following paths in this way, the nearest-neighbor chain algorithm merges its clusters in a different order than methods that always find and merge the closest pair of clusters. However, despite that difference, it always generates the same hierarchy of clusters.

The nearest-neighbor chain algorithm constructs a clustering in time proportional to the square of the number of points to be clustered. This is also proportional to the size of its input, when the input is provided in the form of an explicit distance matrix. The algorithm uses an amount of memory proportional to the number of points, when it is used for clustering methods such as Ward's method that allow constant-time calculation of the distance between clusters. However, for some other clustering methods it uses a larger amount of memory in an auxiliary data structure thatwith which it keeps to keep track of the distances between pairs of clusters.

==Background==