We will cover essential techniques for identifying recurring item combinations in large datasets. Learners explore the Apriori and FP-growth algorithms and learn to assess the significance of discovered associations using metrics like support, confidence, and lift. Participants gain insights into efficiently extracting meaningful patterns from transactional data through practical examples.

We will explore some domains where ARM techniques find practical utility beyond their traditional uses, for example, in recommendation systems or classification tasks. By uncovering hidden patterns and relationships within complex datasets, ARM offers valuable insights and facilitates informed decision-making across various fields, ultimately driving innovation and efficiency.