K-Means

K-Means is an unsupervised machine learning algorithm used for clustering data into groups based on similarity. It partitions a dataset into K distinct, non-overlapping clusters by minimizing the variance within each cluster. The algorithm iteratively assigns data points to the nearest cluster centroid and updates centroids until convergence.

Also known as: K-Means Clustering, K Means, KMeans, Lloyd's Algorithm, K-means++

Why learn K-Means?

Developers should learn K-Means for tasks like customer segmentation, image compression, or anomaly detection where grouping unlabeled data is needed. It's particularly useful in exploratory data analysis, recommendation systems, and preprocessing for other ML algorithms due to its simplicity and efficiency with large datasets.

Learning Resources

• docs: Scikit-learn Documentation: K-Means
• video: K-Means Clustering Algorithm - StatQuest Video
• course: Coursera: Machine Learning by Andrew Ng (includes K-Means)
• tutorial: Towards Data Science: K-Means Explained
• book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (Book)