"K-Means never fails", they said... - Quantdare

It is known that data mining algorithms are not perfect and they can fail under certain conditions. K-Means is an example of that triviality but there is a good alternative, K-Medoids. In a previous post, "Machine Learning: A Brief Breakdown" we already mentioned that K-Means is the cluster analysis algorithm par excellence and it is one of the most important data mining and machine learning techniques; even psanchezcri used it to analyze the direction of a financial time series, in his post "Returns clustering with K-means algorithm". Nevertheless, it's difficult to find discussions about the algorithm's unexpected results in certain cases. The algorithm documentation is too broad in Internet, so the main objective of this post is to focus on showing a financial example of the problem.