K Means Clustering Algorithm: Explained

Classification problems are solved by objective segmentation and subjective segmentation.

A non technical explanation ( http://dni-institute.in/blogs/segmentation-a-perspective-2/ ) on when to use subjective segmentation technique such as K means clustering and when to use objective segmentation methods such as Decision Tree.

One of the most frequently used unsupervised algorithms is K Means. K Means Clustering is exploratory data analysis technique. This is non-hierarchical method of grouping objects together.

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).

In this blog, we aim to explain the algorithm in  a simple steps and with an example.

Business Scenario: We have height and weight information. Using these two variables, we need to group the objects based on height and weight information.

If you look at the above chart, you will expect that there are two visible clusters/segments  and we want these to be identified using K Means algorithm.

Read further on the steps in K Means Clustering