The idea behind clustering is very simple—group similar things together. Nonetheless, there are many different ways to perform this simple task and none are one-size fits all. It's hard to narrow down the playing field for clustering. There are countless real-world applications and many more to be unveiled.
Scientists Garibaldi and Wang published in 2005 a paper showing how clustering could aid cancer diagnosis. For a long time, the industry has been using it to draw recommendations, segment markets, and detect fraud. Social media can be found in the hall of traditional uses of clustering.
In this section, we are about to check the practical concerns of running a hierarchical clustering with R. Different than k-means clustering, ...
