11 K-means clustering

This chapter covers

  • Developing a K-means clustering algorithm
  • Computing and visualizing optimal cluster counts
  • Understanding standard deviations and computing z-scores
  • Creating Cleveland dot plots

Our primary purpose in this chapter is to demonstrate how to develop a K-means clustering algorithm. K-means clustering is a popular unsupervised learning method and multivariate analysis technique that enables purposeful and made-to-order strategies around smart clusters, or groups, cut from the data. Unsupervised learning is a learning method where the goal is to find patterns, structures, or relationships in data using only input variables and therefore no target, or output, data. By contrast, supervised learning methods use ...

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