Eliciting structure in data

A. Holst, M.-R. Bouguelia, O. Görnerup, S. Pashami, A. Al-Shishtawy, G. Falkman, A. Karlsson, Alan Said, J. Bae, S. Girdzijauskas, S. Nowaczyk, A. Soliman. 2019, "Eliciting structure in data". Joint Proceedings of the ACM IUI 2019 Workshops.

Abstract

This paper demonstrates how to explore and visualize different types of structure in data, including clusters, anomalies, causal relations, and higher order relations. The methods are developed with the goal of being as automatic as possible and applicable to massive, streaming, and distributed data. Finally, a decentralized learning scheme is discussed, enabling finding structure in the data without collecting the data centrally.