In This Chapter
Understanding the basics of clustering and classification
Clustering your data with the k-means algorithm and kernel density estimation
Getting to know hierarchical and neighborhood clustering algorithms
Checking out decision tree and random forest algorithms
Data scientists use clustering to help them divide their data into subsets and classification methods to help them build predictive models that they can then use to forecast the classification of future data points. The basics behind clustering and classification are relatively easy to understand, but things get tricky fast when you get into using some of the more advanced algorithms. In this chapter, I introduce the basics behind clustering and classification. I follow that by introducing several nuanced algorithms that offer clustering and classification solutions to meet your requirements, based on the specific characteristics of your feature dataset.
To grasp advanced methods for use in clustering your data, ...