In this chapter, we covered the prerequisites on the data format for the implementation of clustering algorithms and a few major clustering techniques, such as the centroid-based clustering algorithm, hierarchical clustering, and model-based and density-based clustering algorithms. We discussed about a few methodologies to evaluate the outcome of a clustering algorithm and various use cases across multiple fields that can be solved with the implementation of a clustering algorithm.
In the next chapter, we will demonstrate why regression models are used, the difference between a logistic regression and linear regression, and how to implement regression models using R. We will also explore the various methods used to check fit accuracy, the different methodologies that can be used to improve the accuracy of the model, and understand the output of regression models.