Abstract
This chapter introduces the FP-growth algorithm for extracting frequent itemsets from a database of transactions. First the database is processed to produce a data structure called a FP-tree, then the tree is processed recursively by constructing a sequence of reduced trees known as conditional FP-trees, from which the frequent itemsets are extracted. The algorithm has the very desirable feature of requiring only two scans through the database.
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Notes
- 1.
This rather convoluted way of describing the generation of the itemsets {p}, {m}, {b}, {a}, {c} and {f} is for consistency with the description of the generation of two-item, three-item etc. itemsets that follows.
- 2.
All the single item itemsets must inevitably be frequent, as the items in the initial tree were selected from those in the transaction database on that basis. However this will often not be the case as we go on to use findFrequent recursively to analyse reduced versions of the FP-tree.
Reference
Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. SIGMOD Record, 29(2), 1–12. Proceedings of the 2000 ACM SIGMOD international conference on management of data, ACM Press.
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Bramer, M. (2013). Association Rule Mining III: Frequent Pattern Trees. In: Principles of Data Mining. Undergraduate Topics in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-4884-5_18
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DOI: https://doi.org/10.1007/978-1-4471-4884-5_18
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