Hierarchical clustering
2016-08-23
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In data mining, hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types:[citation needed]
Agglomerative: This is a "bottom up" approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.
Divisive: This is a "top down" approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.
In general, the merges and splits are determined in a greedy manner. The results of hierarchical clustering are usually presented in a dendrogram.
In the general case, the complexity of agglomerative clustering is O(n^3), which makes them too slow for large data sets. Divisive clustering with an exhaustive search is O(2^n), which is even worse. However, for some special cases, optimal efficient agglomerative methods (of complexity O(n^2)) are kno
Agglomerative: This is a "bottom up" approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.
Divisive: This is a "top down" approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.
In general, the merges and splits are determined in a greedy manner. The results of hierarchical clustering are usually presented in a dendrogram.
In the general case, the complexity of agglomerative clustering is O(n^3), which makes them too slow for large data sets. Divisive clustering with an exhaustive search is O(2^n), which is even worse. However, for some special cases, optimal efficient agglomerative methods (of complexity O(n^2)) are kno
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