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Apache Mahout Clustering Designs by Ashish Gupta

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Chapter 5. Understanding Model-based Clustering

In the previous chapters, we discussed K-means, Fuzzy K-means, and Canopy clustering. In this chapter, we will discuss the model-based clustering algorithm. Model-based clustering is used to overcome some of the deficiencies that can occur in the K-means or Fuzzy K-means algorithms. We will discuss the following topics in this chapter:

  • Learning model-based clustering
  • Understanding Dirichlet clustering
  • Understanding topic modeling

Learning model-based clustering

In model-based clustering, we assume that data is generated by a model and tries to get the model from the data. The right model will fit the data better than other models.

In the K-means algorithm, we provide the initial set of clusters and K-means ...

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