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