What is the purpose of cluster analysis?

The purpose of cluster analysis is fundamentally about understanding the inherent structure in data. By grouping terms or data points based on specific variables, cluster analysis seeks to identify natural groupings or clusters within the dataset where similarities exist. This method is particularly useful in exploratory data analysis as it allows researchers to discover patterns and relationships that may not be immediately apparent through other means.

In practical terms, the outcome of cluster analysis can aid in various applications such as market segmentation, social network analysis, organization of computing clusters, and many more areas where identifying distinct groups is beneficial. The underlying assumption is that data points within a cluster are more similar to each other compared to those in other clusters, facilitating deeper insights and more targeted decision-making processes.

This focus on grouping differentiates it from other methods, highlighting its unique role in data analysis. Such clustering provides valuable insights that can drive strategic planning and data-driven decision-making.