Hierarchical clustering

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

Strategies

Strategies for hierarchical clustering generally fall into two types:

• Divisive: This type works on the assumption that all the feature vectors form a single set and then hierarchically go on dividing this group into different sets.
• Agglomerative: In this type partitions are visualized using a tree structure called dendrogram.

Advantages vs disadvantages

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.

Disadvantages

• The hierarchical algorithm can never do any previous steps throughout the algorithm
• The time complexity for the clustering can result in very long computation times in comparison with efficient algorithms such as K-means.
• If we have a large data set, it can become difficult to determine the correct number of clusters by the dendrogram.