Mastering Tableau 2018.1, Second Edition 8.4: Clustering Data in Tableau

The video tutorial explains how to identify outliers using K-means clustering in Tableau. It covers creating scatter plots, applying clustering features, and editing clusters. The tutorial also describes how to analyze cluster inputs and outputs, and understand model values like P value and F statistic. The video concludes with a brief mention of the next topic, Pareto charts.

What algorithm is used for detecting outliers in the discussed method?

• Agglomerative Clustering

• K-means Clustering

• Hierarchical Clustering

• DBSCAN

What is the purpose of creating a scatter plot in the context of clustering?

• To determine the number of clusters

• To automatically detect outliers

• To calculate the mean of the dataset

• To visualize the distribution of data points

How can you change the number of clusters in Tableau?

• By changing the color scheme

• By using the number of cluster option

• By editing the data source

• By adjusting the scatter plot dimensions

What can you learn from the 'describe cluster' feature?

• The color of each cluster

• The historical data trends

• The inputs and normalization details

• The geographical location of clusters

Which model values are mentioned for understanding the cluster analysis?

• Standard Deviation and Variance

• Mean and Median

• P-value and F-statistic

• R-squared and Adjusted R-squared