A dendrogram is a network structure. It is constituted of a root node that gives birth to several nodes connected by edges or branches. The last nodes of the hierarchy are called leaves.
Two type of dendrogram exist, resulting from 2 types of dataset:
1- A hierarchic dataset provides the links between nodes explicitly.
2- The result of a clustering algorithm can be visualized as a dendrogram.
Dendrograms are often used to depict the strength of clustering in a matrix. In other words, it shows the hierarchical relationship between objects.
The greater the difference in height, the more dissimilarity. But in some dendrograms, shape and color are also used to help people recognize hierarchy and clusters faster.
Pro:
-Good to allocate objects to clusters.
-The height of the branches often shows similarity/dissimilarity between two objects.
Con:
-The shape of the dendrogram does not explicitly decide the total amount of existing clusters. It cannot tell you how many clusters you should have.
-The height that shows similarity is not always true to original data.
-One two objects join they cannot be seperated.
Calculation: Hierarchical Clustering Algorithms
Dendrogram is usually the visual output of hierarchical clustering. Hierarchical clustering can be performed with either a distance matrix or raw data.
Examples of algorithms used for clustering methods: single linkage, complete linkage, simple average, centroid, median, etc.
Good Examples:
Bad Examples:
