Clustering 101: How to Choose the Right Algorithm for Your Application

Perhaps one of the first machine learning algorithms anyone needs to go through during their data science journey is clustering algorithms. These algorithms are quite well-known among data scientists regardless of their application scope or research topic. Whenever you're working on a data science project, the chances are you will -- at some point, to some extent -- use various clustering techniques to either prepare your data for further analysis or whether initial insights from the data. Clustering techniques can be convenient in preparing and organizing unstructured and unclassified data for further analysis. The reason behind such algorithms' fame is that they provide a simple and fast approach to perform an initial analysis of the data and gain valuable insights into the nature of that data.