Clustering Algorithms: From Start To State Of The Art

It's not a bad time to be a Data Scientist. Serious people may find interest in you if you turn the conversation towards "Big Data", and the rest of the party crowd will be intrigued when you mention "Artificial Intelligence" and "Machine Learning". Even Google thinks you're not bad, and that you're getting even better. There are a lot of'smart' algorithms that help data scientists do their wizardry. It may all seem complicated, but if we understand and organize algorithms a bit, it's not even that hard to find and apply the one that we need. Courses on data mining or machine learning will usually start with clustering, because it is both simple and useful. It is an important part of a somewhat wider area of Unsupervised Learning, where the data we want to describe is not labeled.