Unsupervised Clustering with Apache Spark 2.0

In this chapter, we will cover:

• Building a KMeans classification system in Spark 2.0
• Bisecting KMeans, the new kid on the block in Spark 2.0
• Using Gaussian Mixture and Expectation Maximization (EM) in Spark 2.0 to classify data
• Classifying the vertices of a graph using Power Iteration Clustering (PIC) in Spark 2.0
• Using Latent Dirichlet Allocation (LDA) to classify documents and text into topics
• Streaming KMeans to classify data in near real time