{'fraction_non_alphanumeric': 0.014314928425357873, 'fraction_numerical': 0.0, 'mean_word_length': 5.943262411347518, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Hypothesis Testing and Statistically-sound Pattern Mining

The availability of massive datasets has highlighted the need of computationally efficient and statistically-sound methods to extracts patterns while providing rigorous guarantees on the quality of the results, in particular with respect to false discoveries. In this tutorial we survey recent methods that properly combine computational and statistical considerations to efficiently mine statistically reliable patterns from large datasets. We start by introducing the fundamental concepts in statistical hypothesis testing, including conditional and unconditional tests, which may not be familiar to everyone in the data mining community. We then explain how the computational and statistical challenges in pattern mining have been tackled in different ways. Finally, we describe the application of these methods in areas such as market basket analysis, subgraph mining, social networks analysis, and cancer genomics.