In
data mining and
statistics,
hierarchical clustering (also called
hierarchical cluster analysis or
HCA) is a method of
cluster analysis which seeks to build a
hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types:
- 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.