Agglomerative hierarchical clustering algorithm
Hierarchical clustering algorithms create a hiearchical decomposition of the data set using some criterion. The agglomerative (bottom-up) method merges clusters iteratively.
- Place each object in its own cluster
- Merge these atomic clusters into larger and larger clusters
- Continue until all objects are in a single cluster
- Most hierarchical methods belong in this category; they differ only in their definition of between-cluster similarity.
AGNES (Agglomerative Nesting)
Use the Single-Link method and a dissimilarity matrix. We merge nodes that have the least dissimilarity and go on in non-descending fashion. Eventually all nodes belong to the same cluster.
Practicality
This could be used to segment MRI data into 'like' clusters. Another algorithm could then be used to determine a tissue-labeling for the 'like' clusters individually.
page revision: 1, last edited: 29 Apr 2008 03:52