Clustering Fundamentals K-Means Unsupervised Learning

The -means clustering method is an unsupervised machine learning technique used to identify clusters of data objects in a dataset. -means is one of the oldest and most approachable.

The objective of K-means is to group similar data points together. To achieve this objective, K-means looks for a fixed number () of clusters in a dataset.

If you’re interested in learning how and when to implement -means clustering in Python, then this is the right place. You’ll walk through an end-to-end example of -means clustering using Python, from preprocessing the data to evaluating results.

# Maths behind KMeans Algorithm

#Random Initialization of dataset

# Processing the dataset

# Potting the dataset

# Coding

# Inside clusters

#Algorithm: Coordinate Descent is a mixture of E Step and M Step mentioned in the code

# Plotting the clusters

--

--

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store