Finding high-value patterns in transactions
Looking for valuable patterns in transactions can provide valuable insights. In this blog post, we explore an alternative approach to market basket analysis aimed at uncovering high-value patterns instead of just the most common ones. We’ll delve into different pattern mining problems and analyze a real-world example. For the full code, check out the GitHub repository. All visualizations featured in this post are crafted by the author.
If you’re new to pattern mining concepts, you may want to check out my introductory article first.
Pattern mining revolves around uncovering patterns in data, often represented as sets or sequences. For instance, in website clickstream analysis, the sequence of user actions is crucial. However, in some scenarios, like the one we’ll discuss, item ordering is irrelevant. We aim to identify all items in a transaction along with their occurrence frequency.
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Preprocessing
Data preprocessing is a critical step before applying pattern mining algorithms. The data is typically reformatted to meet the requirements of the mining algorithm. In this case, data is transformed into a transactional format suitable for use with the PAMI library for high-utility pattern mining.