A Practical Guide on K-Means Clustering

Going beyond the theory and getting the best out of your K-Means clustering algorithm

Table of contents

1. What is KMeans?
   a. Python implementation
2. Things to know before using KMeans
   a. K-Means cannot handle non-globular structure
   b. K-Means is sensitive to outliers
   c. Should you scale your data before using KMeans?
3. How do you measure the performance of your model?

Note — The datasets used in the article are generated using sklearn’s make_blobs and make_moons methods …

clustering data analysis data science k-means k-means-clustering machine learning programming