Very large databases are a major opportunity for science and data analytics is a remarkable
new field of investigation in computer science. The effectiveness of these tools is used to …

The proposal of Reshef et al.(2011) is an interesting new approach for discovering non-
linear dependencies among pairs of measurements in exploratory data mining. However, it …

In order to measure whether and how things are related, statistical correlation analysis
comes into being. Among them, Pearson coefficients, Spearman and Kendall coefficients …

Finding latent patterns in high dimensional data is an important research problem with
numerous applications. Existing approaches can be summarized into 3 categories: feature …

Correlation measures are a key element of statistics and machine learning, and essential for
a wide range of data analysis tasks. Most existing correlation measures are for pairwise …

Calculating correlation coefficients is one of the most used measures in data science.
Although linear correlations are fast and easy to calculate, they lack robustness and …

Dependence across multiple financial markets, such as stock and foreign exchange rate
markets, is high-dimensional, contains various relationships, and often presents complicated …

The heterogeneity and complexity of multivariate characteristics poses a unique challenge
to visual exploration of multivariate scientific data sets, as it requires investigating the usually …

Classical dependence measures such as Pearson correlation, Spearman's ρ, and Kendall's
τ can detect only monotonic or linear dependence. To overcome these limitations, Székely et …