Statistically sound pattern discovery harnesses the rigour of statistical hypothesis testing to
overcome many of the issues that have hampered standard data mining approaches to …

Discriminative sequential patterns are sub-sequences whose occurrences exhibit significant
differences across sequential data sets with different class labels. The discovery of such …

The extraction of patterns displaying significant association with a class label is a key data
mining task with wide application in many domains. We study a variant of the problem that …

Finding statistically significant high-order interactions in predictive modeling is important but
challenging task because the possible number of high-order interactions is extremely large …

We present SPuManTE, an efficient algorithm for mining significant patterns from a
transactional dataset. SPuManTE controls the Family-wise Error Rate: it ensures that the …

Abstract Information theoretic feature selection methods quantify the importance of each
feature by estimating mutual information terms to capture: the relevancy, the redundancy …

We are interested in discovering those patterns from data with an empirical frequency that is
significantly differently than expected. To avoid spurious results, yet achieve high statistical …

Discovering patterns in data that best describe the differences between classes allows to
hypothesize and reason about class-specific mechanisms. In molecular biology, for …

The identification of significant patterns, defined as patterns whose frequency significantly
deviates from what is expected under a suitable null model of the data, is a key data mining …

Motivation Most modern intensive care units record the physiological and vital signs of
patients. These data can be used to extract signatures, commonly known as biomarkers, that …