Hierarchical cluster analysis in r

Cluster analysis is a foundational unsupervised learning methodology that facilitates the discovery of inherent structural patterns within multidimensional datasets through the systematic grouping of similar observations based on their intrinsic characteristics and spatial relationships.

Through a variety of approaches such as centroid-based partitioning (k-means), hierarchical agglomeration, and density-based clustering (DBSCAN), practitioners can uncover meaningful data segments that can help identify natural groupings, outliers, and underlying patterns that may not be immediately apparent through traditional analytical methods.

The implementation of cluster analysis in R provides researchers and data scientists with a robust computational framework for exploring these latent structures, offering both statistical rigor and visual insight through a comprehensive set of clustering algorithms.

Visualization of clustered results can further help shed light on our data.

In this article, we will learn how to use these methods in R.

Preparing the Data

First, we will load a datase

how to do cluster analysis in spss

how do you do a cluster analysis