Efficient detection of multivariate correlations with different correlation measures

Correlation analysis is an invaluable tool in many domains, for better understanding the data and extracting salient insights.
Most works to date focus on detecting high pairwise correlations. A generalization of this problem with known applications
but no known efficient solutions involves the discovery of strong multivariate correlations, i.e., finding vectors (typically in
the order of 3–5 vectors) that exhibit a strong dependence when considered altogether. In this work, we propose algorithms for
detecting multivariate correlations in static and streaming data. Our algorithms, which rely on novel theoretical results, support
four different correlation measures, and allow for additional constraints. Our extensive experimental evaluation examines the
properties of our solution and demonstrates that our algorithms outperform the state-of-the-art, typically by an order of
magnitude.