Distributed Computing Seminar Lecture 4 provided an overview of clustering algorithms and techniques. It discussed how clustering is used to group related data in applications like Google News and Amazon. It described hierarchical and partitional clustering algorithms and the k-means clustering algorithm. The lecture also introduced canopy clustering as a preliminary step that can help parallelize computation for clustering large datasets using MapReduce. It provided an example of how to efficiently partition a large movie ratings dataset into clusters using canopy clustering, k-means clustering, and MapReduce.

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