Abstract. Correlation analysis is one of the key elements of statistics. It is widely studied from a theoretical point of view and has various applications in data analysis. Whereas most existing measures can only detect pairwise correlations between two dimensions, modern analysis aims at detecting correlations in multi-dimensional spaces.

We propose mac, a novel multivariate correlation measure designed for discovering multi-dimensional patterns. It belongs to the powerful class of maximal correlation analysis, for which we propose a generalization to multivariate domains. We highlight the limitations of current methods in this class, and address these with mac. Our experiments show that we outperform existing solutions, are robust to noise, and discover interesting and useful patterns.