The step-by-step approach using K-Means Clustering using SAS

Here, in this article, I am trying to explain the K-Means clustering algorithm from scratch and the implementation of this using SAS.

Hello Data lovers,

A few days back, I was exploring the projects which I did during my 9+ years of tenure in the field of analytics and statistical modeling. I found one of the interesting projects from one of my previous organization — Segmentation.

I used K-Means clustering to segment households, products, and so on 7 years ago.

Here, in this article, I am trying to explain the K-Means clustering algorithm from scratch and the implementation of this using SAS.

Disclaimer: This article is aimed to share my knowledge of K-Means clustering. This does not include any project-related data, codes, and so on. SAS codes used in this article are purely based on publically available datasets and not relate to any company project. The reader of this article must have a background in Statistics and SAS

Let’s start now!!!

Segmentation Overview: Before we understand what K-Means clustering is, let’s see why to do segmentation and how it is helpful to any business.

Because customers are different.

That’s all!!! We are not here to master on segmentation. Let’s now see how we can segment the group of customers statistically. There are many statistical methods available. Here, we only consider the K-Means clustering method. We will understand this method in three steps as follow:

*Step 1: Defining the number of clusters: *K-means clustering is a type of non-hierarchical clustering where K stands for K number of clusters. Different algorithms are available to get the optimum number of clusters. We will discuss this as we progress.

*Step 2: Define the Centroid of each cluster: *K-means clustering is an iterative procedure to define the clusters. This step basically the starting point as a center of each cluster. Say, we have

unsupervised-learning clustering analytics machine-learning

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