R Tutorial: Introduction to model-based clustering

Want to learn more? Take the full course at https://learn.datacamp.com/courses/mixture-models-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi, I'm Victor Medina. I'm a researcher at SBIF and I really enjoy using R to extract valuable insights from data. This course will introduce you to mixture models, one of the most interesting and useful statistical frameworks to cluster and extract patterns from data. First, let's clarify what do we mean when we talk about clustering. Simply, clustering is the procedure of partitioning a collection of observations into a set of meaningful subclasses or clusters. By meaningful, we suggest that all the observations belonging to a cluster share some similarities but are essentially distinct from the other clusters' observations. This procedure lets us explore the natural structure in a data set. Cluster analysis is used among several disciplines. In medicine, for example, is used to analyze different types of tissues in a medical scan as an aid to the diagnosis of disease. In business, is used to discover different groups of customers in order to develop targeted marketing programs. And in social sciences, it can be used to identify zones in a city by the type of crimes to manage law enforcement resources more effectively, as we will see later. There are many approaches to clustering, and the choice will depend on the aim of the analysis. Widely used are the partitioning techniques, the hierarchical techniques, and the model-based methods. The first tries to find the centres of the clusters and assign each observation exclusively to the closest cluster. Example of this approach is Kmeans. The second connects the observations based on their similarity to start forming the clusters, which means the number of clusters is related to the number of connections we have made. In the lowest level for example, when we have no connections between the observ