Abstract:

Discovering interesting and useful patterns in symbolic data has been the goal of numerous studies. Several algorithms have been designed to extract patterns from data that meet a set of requirements specified by a user. Although many early research studies in this domain have focused on identifying frequent patterns (e.g. itemsets, episodes, rules), nowadays many other types of interesting patterns have been proposed and more complex data types and pattern types are considered. Mining patterns has 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 current problems and state-of-the-art techniques for identifying interesting patterns in symbolic data. Moreover, this talk will discuss how to bridge traditional pattern mining techniques with machine learning. Topics that will be discussed include high utility patterns, locally interesting patterns, and periodic patterns. Lastly, the SPMF open-source software will be mentioned and opportunities related to the combination of pattern mining algorithms with traditional artificial intelligence techniques will be discussed.