Clustering Fundamentals K-Means Unsupervised Learning

The k-means clustering method is an unsupervised machine learning technique used to identify clusters of data objects in a dataset. k-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 (k) of clusters in a dataset.

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