Skip to Main Content
Statistical Tableau
book

Statistical Tableau

by Ethan Lang
May 2024
Beginner to intermediate content levelBeginner to intermediate
316 pages
7h 54m
English
O'Reilly Media, Inc.
Book available
Content preview from Statistical Tableau

Chapter 11. Clustering in Tableau

Oftentimes, you will find yourself wanting to better understand how things relate to one another. What groups of products sell well when paired together? How should I market to certain groups of customers? Are there anomalies in my data? If you’re asking these types of questions, then clustering is a great model to start finding answers. The primary objective of clustering is to partition a dataset into subgroups or clusters. The models achieve this by partitioning the data so that the data points in one cluster are more similar to each other than another cluster’s data points.

There are many different clustering models, each with its own pros and cons. In Tableau, the algorithm that is built in for clustering is called k-means. K-means is a widely used model that provides an automated approach to grouping data. Unlike the other regression models, k-means is also an unsupervised model, which means having a normal distribution is not an assumption for this model.

In this chapter, you will learn how the k-means model works, the difference between supervised and unsupervised models, and how to implement k-means in Tableau.

What Is K-Means Clustering?

K-means clustering is a versatile, unsupervised technique that can be applied to various domains and problems. Examples of how you could use k-means clustering include:

Customer segmentation

K-means clustering is used to group customers based on their purchasing behavior, demographics, or other ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Data Science for Business

Data Science for Business

Foster Provost, Tom Fawcett
R for Data Science, 2nd Edition

R for Data Science, 2nd Edition

Hadley Wickham, Mine Çetinkaya-Rundel, Garrett Grolemund

Publisher Resources

ISBN: 9781098151782Errata Page