About this document
K-means Clustering Explained by lishay2k25 is a document available to read on EtoBox.
The document provides an overview of clustering, specifically focusing on the K-means algorithm, which is an unsupervised learning technique that groups similar data points into a specified number of clusters. It outlines the steps involved in the K-means process, including initializing centroids, assigning points to clusters, and recomputing cluster centers until convergence. Additionally, it includes a code example using Python
- Author
- lishay2k25
- Language
- EN