Introduction
The majority of readers will be familiar with Wal-Mart moving beer next to diapers in its stores because it found that the purchase of both products is highly correlated. This is one example of what data mining is about; it can help us find how items are associated in a transaction dataset. Using this skill, a business can explore the relationship between items, allowing it to sell correlated items together to increase sales.
As an alternative to identifying correlated items with association mining, another popular application of data mining is to discover frequent sequential patterns from transaction datasets with temporal information. This can be used in a number of applications, including predicting customer shopping sequence order, web click streams and biological sequences.
The recipes in this chapter cover creating and inspecting transaction datasets, performing association analysis with the Apriori algorithm, visualizing associations in various graph formats, and finding...
