What is the KMeans Clustering Algorithm and How is it Used to Analyze Data?

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This article provides a brief explanation of
the KMeans Clustering algorithm.

What is the KMeans
Clustering algorithm?

The KMeans Clustering algorithm is a process by which objects are classified into number of groups so that they are as much dissimilar as possible from one group to another, and as much similar as possible within each group. KMeans Clustering is a grouping of similar things or data. For example, objects within group 1 (cluster 1) shown in image below should be as similar as possible.

But there should be much difference between an
object in group 1 and group 2.

The attributes of objects decide which objects
should be grouped together. This method is used to find groups that have not
been explicitly labeled in the data, and it can be used to confirm business
assumptions about what types of groups exist, or to identify unknown groups in
complex data sets. Once the algorithm has been run and the groups are defined,
any new data can be easily assigned to the correct group.

How Does an Enterprise
Use the KMeans Clustering Algorithm to Analyze Data?

In order to understand how best to make use of
this algorithm; let’s look at some general examples, followed by some business
use cases.

  • Loan applicants in a bank might
    be grouped as low, medium, and high risk applicants based on applicant
    age, annual income, employment tenure, loan amount, the number of times a
    payment is delinquent etc.
  • A movie ticket booking website
    can group users into frequent ticket buyers, moderate ticket buyers and
    occasional ticket buyers, based on past movie ticket purchases.

KMeans Clustering can be applied to segment
customers by purchasing history, segment users by the activities they perform
on a website, define demographic profiles based on interests, and recognize
market patterns.

Use Case One

Business Problem: Organizing customers into
groups/segments based on similar traits, product preferences and expectations.
Segments are constructed on basis of the customers’ demographic
characteristics, psychographics, past behavior and product use behaviors.

Business Benefit: Once the segments are identified,
marketing messages and even products can be customized for each segment. The
better the segment(s) chosen for targeting by a particular organization, the
more successful it is assumed to be in the market place.

Use Case Two

Business Problem: Discount Analysis and Customer Retention
will help the organization to target discounts to specific customers and the
business will need to visualize ‘segments of sales group based on discount
behavior’ and ‘customer churn to identify segments of customers on the verge of
leaving’.

Business Benefit: The business marketing team can focus on
risky customer segments in an efficient way in order to avoid losing those
customers. Sales team segments that are facing challenges based on any current
discounting strategy can be identified and a deal negotiation strategy can be
improved and optimized.

The KMeans Clustering algorithm is very useful
in identifying patterns within groups and understanding the common
characteristics to support decisions regarding pricing, product features, risk
within certain groups, etc.

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