This document provides an overview of k-means clustering, including what it is, how it works, and how to apply it using the CRISP-DM method. The learning objectives are to explain k-means clustering, demonstrate proper data formatting, and interpret cluster results. An example applies k-means to insurance policyholder data to identify groups at high risk for heart disease. The clusters help target communications to promote a weight and cholesterol management program.

1. BAS 250
Lesson 3: K-Means Clustering

2.  Effectively employ the CRISP-DM method
 Develop a k-means cluster data mining model
 Interpret output generated by model
This Week’s Learning Objectives

3.  Explain what k-Means clusters are, how they are
found, and their benefits
 Demonstrate the necessary format for data in
order to create k-Means clusters
 Interpret the clusters generated by a k-Means
model and explain their significance, if any
K-Means Clustering

4.  Clustering means: “Grouping of data or dividing a large data set
into smaller data sets of some similarity”
 The “k” in k-Means clustering stands for some number of groups, or
clusters – You can control over these (supervised learning).
 Enables the user to define natural groups between data sets by
comparing the means of their individual attribute values
 Means are susceptible to undue influence by extreme outliers, so
watching for inconsistent data is very important with k-Means
K-Means Clustering

5.  k-Means algorithm samples observations and then compares the
other attributes in the data set to that sample’s means
 Process is repeated in order to ‘circle-in’ on the best matches and
then formulate groups of observations which become clusters as
the means become more and more similar
 Sometimes takes a while to run, especially if using a large number
of ‘max runs’ or seeking a large number of clusters (k)
K-Means Clustering

6. K-Means Clustering

7.  For every business problem going forward, you will work
through the CRISP-DM method to complete your work.
K-Means Clustering: CRISP-DM

8.  Context:
o You work for a major health insurance provider and have been
asked to create a weight and cholesterol management program
for policy holders to reduce policy payouts due to heart disease.
You have a limited budget to communicate with potential policy
holders who would benefit from such a program. Your message
must be targeted to those who are most at risk for heart disease
due to weight issues and high cholesterol.
K-Means Clustering

9.  Business Understanding:
o You will need to search through thousands of
policy holders to find groups of people with similar
characteristics and develop programs and
communications that will be relevant to these
groups.
K-Means Clustering

10.  Data Understanding:
o Instead of searching thousands of policy holders, you have access to a clean sample of
roughly 550. There are 3 attributes. Each row is a policy holder. If gender = 1, then male. If
gender = 0, then female.

K-Means Clustering

11.  Data Preparation:
o None of the values seem to be inconsistent.
 No missing values and the standard deviations are reasonable.
K-Means Clustering

12.  Data Modeling:
o We will use k-means clustering to determine the natural
groups. We will not be predicting who will have heart
disease, as k-means is not predictive.
o We want to know more than 2 clusters (high and low risk of
heart disease), as there are likely a number of different
types of groups.
o For this exercise, we will use 4 potential groups.
K-Means Clustering

13. K-Means Clustering

14. Observations:
• Clusters are fairly
balanced.
• We will keep these
groups for
evaluation.
 Once we run the cluster process…below is the
output.
K-Means Clustering

15. Evaluation:
 The Centroid Table shows the means for each attribute in the four (k)
clusters
K-Means Clustering

16.  To see who these policy holders are, you can see more details by
going to “Folder View” and clicking on “cluster_3”.
 Observation #6 refers to a policy holder’s information.
K-Means Clustering

17. Deployment:
 To deploy the information from the analysis, we will go back to the design tab…
Add a filter process
to choose only
“cluster_3” using the
attribute_value_filter
K-Means Clustering

18. Deployment:
The results from filtering on cluster_3…
K-Means Clustering

19. Deployment:
You can now go back to your company’s database and issue a
SQL query to pull all records…
SELECT First_Name, Last_Name, Policy_Num, Address, Phone_Num
FROM PolicyHolders_view
WHERE Weight >= 167
AND Cholesterol >= 204
AND Gender = 1;
K-Means Clustering

20. Deployment:
 By targeting the highest risk of heart disease group, you can reduce
the payouts, thus increasing profits for your company.
Note: Your next targeted communication would have been “cluster_2”. This
group is women with a high risk of heart disease. There the message may be
communicated differently from men.
K-Means Clustering

21.  k-Means does not necessarily predict values, it simply
takes known indicators from the attributes in a data set
and groups them together based on those attributes’
similarity to group averages
 It helps the user to understand where one group
begins and the other ends- in other words, where the
natural breaks occur between groups in a data set
K-Means Clustering: Summary

22.  Effectively employ the CRISP-DM method
 Develop a k-means cluster data mining model
 Interpret output generated by models
Summary - Learning Objectives

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