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Apache Mahout Essentials by Jayani Withanawasam

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Chapter 2. Clustering

This chapter explains the clustering technique in machine learning and its implementation using Apache Mahout.

The K-Means clustering algorithm is explained in detail with both Java and command-line examples (sequential and parallel executions), and other important clustering algorithms, such as Fuzzy K-Means, canopy clustering, and spectral K-Means are also explored.

In this chapter, we will cover the following topics:

  • Unsupervised learning and clustering
  • Applications of clustering
  • Types of clustering
  • K-Means clustering
  • K-Means clustering with MapReduce
  • Other clustering algorithms
  • Text clustering
  • Optimizing clustering performance

Unsupervised learning and clustering

Information is a key driver for any type of organization. However, ...

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