Clustering Flower Species Data in Tableau

This article explores how clustering, a powerful analytical technique, can help organizations uncover hidden patterns and make data-backed decisions. It demonstrates Tableau's built-in clustering capabilities using a practical example of clustering flower species data.

Details

The article starts by explaining the concept of clustering, which is the process of grouping similar observations or data points based on shared characteristics. It provides a simple business example of a car manufacturer analyzing customer preferences and segmenting them into distinct clusters. The article then discusses why clustering is important in business, as it allows organizations to move from intuition-driven decisions to evidence-based segmentation. Tableau provides clustering capabilities using the K-means algorithm, a centroid-based approach widely used in analytics. The article walks through a practical example of clustering flower species data in Tableau, including loading the dataset, creating a scatter plot, applying clustering, and interpreting cluster quality metrics like F-statistic and P-value. It also covers how to save clusters as a dimension for further analysis. The article concludes by mentioning another example of using Tableau's clustering to segment countries based on world indicators.