Hierarchical clustering: Difference between revisions

Revision as of 01:10, 1 April 2020

Hierarchical clustering, also known as hierarchical cluster analysis, is an algorithm that groups similar objects into groups called clusters.^[1]

Strategies for hierarchical clustering generally fall into two types^[2]:

Hierarchical clustering does not require the number of clusters to be specified.
It is easy to implement.
Hierarchical clustering produces a dendogram, which hlps with understanding the data.

@@ Line 5: / Line 5: @@
 * Divisive:
 * Agglomerative:
+== Advantages vs disadvantages<ref>[https://www.coursera.org/learn/machine-learning-with-python/lecture/h2iAD/more-on-hierarchical-clustering Hierarchical Clustering]Coursera</ref> ==
+=== Advantages ===
+* Hierarchical clustering does not require the number of clusters to be specified.
+* It is easy to implement.
+* Hierarchical clustering produces a dendogram, which hlps with understanding the data.
 == References ==