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Published in:
Introduction to Data Mining Read chapter Read first chapter

2020 | OriginalPaper | Chapter

17. Association Rule Mining II

Author : Prof. Max Bramer

Published in: Principles of Data Mining

Publisher: Springer London

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Abstract

This chapter is concerned with a special form of Association Rule Mining known as Market Basket Analysis, the most common application of which is to relate the purchases made by the customers in a shop. An approach to finding rules of this kind, with support and confidence measures above specified thresholds, is described. This is based on the idea of supported itemsets. The Apriori algorithm for finding supported itemsets is described in detail. Further rule interestingness measures, lift and leverage, which can be used to reduce the number of rules generated are introduced.

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previous chapter Association Rule Mining I
next chapter Association Rule Mining III: Frequent Pattern Trees
Literature
[1]
Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In J. B. Bocca, M. Jarke & C. Zaniolo (Eds.), Proceedings of the 20th international conference on very large databases (VLDB94) (pp. 487–499). San Mateo: Morgan Kaufmann. http://citeseer.nj.nec.com/agrawal94fast.html.
Metadata
Title
Association Rule Mining II
Author
Prof. Max Bramer
Copyright Year
2020
Publisher
Springer London
Book
Principles of Data Mining

Print ISBN: 978-1-4471-7492-9

Electronic ISBN: 978-1-4471-7493-6

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-1-4471-7493-6_17