This document provides an overview of clustering techniques and presents a sample MapReduce implementation of K-Means clustering and Canopy clustering on a large dataset. It discusses how clustering can be used to group large, high-dimensional datasets and describes hierarchical and partitional clustering algorithms. It also outlines the steps taken in the MapReduce implementation to distribute the clustering computation across multiple nodes.

1. Distributed Computing Seminar Lecture 4: Clustering – an Overview and Sample MapReduce Implementation Christophe Bisciglia, Aaron Kimball, & Sierra Michels-Slettvet Summer 2007 Except as otherwise noted, the content of this presentation is © 2007 Google Inc. and licensed under the Creative Commons Attribution 2.5 License.

22. Selecting Canopy Centers

38. Assigning Points to Canopies

44. K-Means Map