In this chapter, we have discussed unsupervised learning. In particular, we have explained hierarchical clustering and k-means clustering. As for R and Python, we have explained several related packages:
- R: rattle, Rmixmod, and randomUniformForest
- Python: scipy.cluster, contrastive, and sklearn
Several real-world examples have also been used to illustrate the applications of these packages in detail.
For the next chapter, we will discuss supervised learning, such as classification, the k-nearest neighbors algorithm, Bayes' classifiers, reinforcement learning, and specific R and Python-related modules, such as RTextTools and sklearn. In addition, we will discuss implementation via R, Python, Julia, and Octave.
