What this book covers

Chapter 1, Understanding Clustering, explains clustering in general. This chapter will further discuss the different distance matrices and how to calculate them.

Chapter 2, Understanding K-means Clustering, introduces K-means clustering and how Mahout can be used for K-means clustering algorithms.

Chapter 3, Understanding Canopy Clustering, introduces Canopy clustering and its uses in Apache Mahout.

Chapter 4, Understanding the Fuzzy K-means Algorithm Using Mahout, talks about the Fuzzy K-means algorithm and how this algorithm works as a preprocessing step for K-means. We will further discuss how to use Mahout for the Fuzzy K-means algorithm.

Chapter 5, Understanding Model-based Clustering, discusses model-based clustering. This chapter further discusses the topic of modeling using Dirichlet clustering.

Chapter 6, Understanding Streaming K-means, introduces the Streaming K-means algorithm, which is used for streaming data. We will further discuss how Mahout can be used for Streaming K-means.

Chapter 7, Spectral Clustering, introduces spectral clustering and how Mahout has implemented spectral clustering.

Chapter 8, Improving Cluster Quality, covers the steps that should be followed to improve cluster quality once you are ready with your clustering algorithm, in detail. It also discusses what techniques Mahout provides to improve cluster quality.

Chapter 9, Creating a Cluster Model for Production, introduces the techniques that should be followed in a production environment while applying the clustering algorithm.