Understanding K-means Clustering and Its Application on a Real Dataset Using Python

In the world of data science, clustering techniques are widely used to group similar data points together, and K-Means is one of the simplest yet powerful clustering algorithms. Whether you are new to data science or looking to explore how K-Means works with a real dataset using Python, this article is for you!

What is K-Means Clustering?

K-means clustering is an unsupervised learning algorithm used to partition a dataset into K distinct clusters. The goal is to minimize the sum of the distances between the data points and their respective cluster centers (centroids).

How K-means Works:

1. Initialization: Randomly choose K initial centroids.
2. Assignment: Assign each data point to the nearest centroid based on the distance metric (typically Euclidean distance).
3. Update: Calculate the new centroids as the mean of all points assigned to each cluster.
4. Iteration: Repeat the assignment and update steps until the centroids no longer change significantly or until a predefined number of iterations is reached.

Application: Real-world example with Python