The k-means algorithm is a cornerstone of unsupervised machine learning, known for its simplicity and trusted for its efficiency in partitioning data into a predetermined number of clusters.

It would be difficult to argue that word embeddings — dense vector representations of words — have not dramatically revolutionized the field of natural language processing (NLP) by quantitatively capturing semantic relationships between words.