Chapter 5. Clustering

With data comprising of several separated distributions, how do we find and characterize them? In this chapter, we will look at some ways to identify clusters in data. Groups of points with similar characteristics form clusters. There are many different algorithms and methods to achieve this with good and bad points. We want to detect multiple separate distributions in the data and determine the degree of association (or similarity) with another point or cluster for each point. The degree of association needs to be high if they belong in a cluster together or low if they do not. This can of course, just as previously, be a one-dimensional problem or multi-dimensional problem. One of the inherent difficulties of cluster finding is determining how many clusters there are in the data. Various approaches to define this exist; some where the user needs to input the number of clusters and then the algorithm finds which points belong to which cluster, and some where...