Mining Similarities and Concepts at Scale.

In machine learning, similarities and abstractions are fundamental for understanding and efficiently representing data. At SICS Swedish ICT, we have developed a domain-agnostic, data-driven and scalable approach for finding intrinsic similarities and concepts in large datasets. This approach enables us to discover semantic classes in text, musical genres in playlists, the genetic code from biomolecular processes and much more.