Discovering interesting and useful patterns in symbolic data has been the subject of numerous studies. It consists of extracting patterns from data that meet a set of requirements specified by a user. Although early research work in this domain have mainly focused on identifying frequent patterns (e.g. itemsets), nowadays many other types of interesting patterns have been proposed and more complex data types and pattern types are considered. Mining patterns have applications in many fields as they provide glass-box models that are generally easily interpretable by humans either to understand the data or support decision-making. This talk will first highlight limitations of early work on frequent pattern mining and provide an overview of state-of-the-art problems and techniques related to identifying interesting patterns in symbolic data. Topics that will be discussed include high utility patterns, locally interesting patterns, periodic patterns, and statistically significant patterns. Lastly, the SPMF open-source software will be mentioned and opportunities related to the combination of pattern mining techniques with traditional artificial intelligence techniques.
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