Chapter 9. Discovering patterns with clustering

This chapter covers
  • k-means, hierarchical clustering, and probabilistic clustering
  • Clustering blog entries
  • Clustering using WEKA
  • Clustering using the JDM APIs

It’s fascinating to analyze results found by machine learning algorithms. One of the most commonly used methods for discovering groups of related users or content is the process of clustering, which we discussed briefly in chapter 7. Clustering algorithms run in an automated manner and can create pockets or clusters of related items. Results from clustering can be leveraged to build classifiers, to build predictors, or in collaborative filtering. These unsupervised learning algorithms can provide insight into how your data is distributed. ...

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