What is hierarchical clustering and how does it work?

What is hierarchical clustering and how does it work?

Hierarchical clustering is a method of cluster analysis that seeks to build a hierarchy of clusters. It can be divided into two types: agglomerative (bottom-up) and divisive (top-down). In agglomerative clustering, each data point starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. In divisive clustering, all data points start in one cluster, and splits are performed recursively as one moves down the hierarchy.

What are the main advantages of using hierarchical clustering?

Some advantages include the ability to visualize the data through dendrograms, no need to specify the number of clusters in advance, and it can capture complex cluster shapes.

Can you explain how to create a dendrogram?

To create a dendrogram, you first perform hierarchical clustering on your data. Then, you can use a plotting library to visualize the clusters. The dendrogram will show the arrangement of clusters and the distances at which they were merged.

What are some common applications of hierarchical clustering?

Common applications include social network analysis, gene expression data analysis, and market segmentation.