A comprehensive guide to understanding the correlation matrix, including its use in identifying and quantifying correlations between variables for future predictions, and how to create such matrices in Python.
This article introduces interpretable clustering, a field that aims to provide insights into the characteristics of clusters formed by clustering algorithms. It discusses the limitations of traditional clustering methods and highlights the benefits of interpretable clustering in understanding data patterns.
