About this document
Unsupervised Learning: Clustering Methods by Antonio Victory is a document available to read on EtoBox.
This document provides an overview of unsupervised learning and clustering algorithms. It begins with definitions of supervised vs. unsupervised learning, then defines clustering as a technique for grouping similar data instances into clusters. The document outlines the k-means clustering algorithm, including initializing centroids, assigning points, recomputing centroids, and stopping criteria. Strengths of k-means are its simplicity and efficiency, while weaknesses include sensitivity to outliers, initial
- Author
- Antonio Victory
- Language
- EN