Discover how Tableau and Python work together to uncover meaningful clusters in high-dimensional data — transforming complex risk patterns into clear, actionable insight.
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Community Speaker
Reserved Seating
Video On Demand
Cluster Analysis in Tableau and Python
, Lead R&D Analyst, Progressive Insurance
Role: Data Analyst
Product: Tableau
Tableau Product: Tableau Desktop
Session Level: Intermediate
Session Type: Breakout
- Tuesday, May 52:50 PM - 3:30 PM PDT(9:50 PM - 10:30 PM GMT)Stage 2 | Content Pavilion, Ground LevelRESERVED SEATING
- Thursday, May 71:00 PM - 1:40 PM PDT(8:00 PM - 8:40 PM GMT)Stage 6 | Content Pavilion, Ground LevelRESERVED SEATING
Session Summary: This session explored the use of Tableau for advanced cluster analysis, focusing on spatial, multivariate, and high-dimensional data from FEMA related to Louisiana's natural disaster losses. The presenter demonstrated univariate and multivariate cluster analysis, leveraging Tableau's built-in K-means clustering and integrating Python through TabPy for more advanced methods like DBSCAN, agglomerative, and Gaussian Mixture Models (GMM). Notable findings in the data set included the identification of two main clusters—coastal and non-coastal parishes—with coastal parishes showing four times higher expected hurricane losses, as well as insights into optimizing the number of clusters using Tableau's built-in methods and external statistical techniques. The session concluded with a discussion of visualizations such as scatter plots and radar charts, emphasizing their effectiveness in presenting clustering results and encouraging attendees to apply these techniques to their own data for actionable insights.
Key Takeaways:
• Cluster analysis in Tableau can effectively simplify complex data, as shown in a FEMA data set by grouping Louisiana's 64 parishes into coastal and non-coastal clusters, with coastal areas experiencing significantly higher economic losses from hurricanes.
• Integrating Tableau with Python via TabPy allows for the use of advanced clustering methods like DBSCAN and GMM, which provide robust results and highlight the importance of testing multiple techniques for consistency.
• Visualizations such as scatter plots and radar charts complement maps by offering clear, non-geographic representations of clustering results, making data insights more accessible and actionable for diverse audiences.
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