The Anatomy of K-Means Clustering

Let's say you want to classify hundreds (or thousands) of documents based on their content and topics, or you wish to group together different images for some reason. Or what's even more, let's think you have that same data already classified but you want to challenge that labeling. You want to know if that data categorization makes sense or not, or can be improved. Well, my advice is that you cluster your data. Information is often darkened by noise and redundancy, and grouping data into clusters (clustering) with similar features is an efficient way to bring some light on.