Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Approximation of Frequent Itemsets

  • Jinze LiuEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_22




Consider an n × m binary matrix D. Each row of D corresponds to a transaction t and each column of D corresponds to an item i. The (t, i)-element of D, denoted D(t, i), is 1 if transaction t contains item i, and 0 otherwise. Let T0 = {t1, t2,…,tn} and I0 = {i1, i2,…,im} be the set of transactions and items associated with D, respectively.

Let D be as above, and let ε r, ε c ∈ [0, 1]. An itemset I ⊆  I 0 is an approximate frequent itemset AFI( ε r, ε c), if there exists a set of transactions T ⊆  T 0 with | T | ≥ minsup | T 0 | such that the following two conditions hold:
  1. 1.

    \( \forall i\in T,\frac{1}{\mid I\mid}\sum_{j\in I}D\left(i,j\right)\ge \left(1-{\upepsilon}_r\right); \)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of KentuckyLexingtonUSA

Section editors and affiliations

  • Jian Pei
    • 1
  1. 1.School of Computing ScienceSimon Fraser Univ.BurnabyCanada