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Introduction to Data Mining Read chapter Read first chapter

2016 | OriginalPaper | Chapter

18. Association Rule Mining III: Frequent Pattern Trees

Author : Prof. Max Bramer

Published in: Principles of Data Mining

Publisher: Springer London

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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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previous chapter Association Rule Mining II
next chapter Clustering
Footnotes
1
This rather convoluted way of describing the generation of the itemsets \(\{\mathrm{p}\}\), \(\{m\}\), \(\{b\}\), \(\{a\}\), \(\{c\}\) 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.
 
Literature
[1]
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. CrossRef
Metadata
Title
Association Rule Mining III: Frequent Pattern Trees
Author
Prof. Max Bramer
Copyright Year
2016
Publisher
Springer London
Book
Principles of Data Mining

Print ISBN: 978-1-4471-7306-9

Electronic ISBN: 978-1-4471-7307-6

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-1-4471-7307-6_18