Database Reference
In-Depth Information
Chapter 6
Negative Association Rules
Luiza Antonie, Jundong Li and Osmar Zaiane
Abstract
Mining association rules associates events that took place together. In
market basket analysis, these discovered rules associate items purchased together.
Items that are not part of a transaction are not considered. In other words, typical
association rules do not take into account items that are part of the domain but that
are not together part of a transaction. Association rules are based on frequencies and
count the transactions where items occur together. However, counting absences of
items is prohibitive if the number of possible items is very large, which is typically
the case. Nonetheless, knowing the relationship between the absence of an item and
the presence of another can be very important in some applications. These rules are
called negative association rules. We review current approaches for mining negative
association rules and we discuss limitations and future research directions.
Keywords
Negative association rules
1
Introduction
Traditional association rule mining algorithms [
11
] have been developed to find pos-
itive associations between items [
4
,
9
,
26
,
14
]. Positive associations are associations
between items existing in transactions (i. e. items that are present and observed). In
market basket analysis, we are generally interested in items that were purchased, and
particularly in items purchased together. The assumption is that items that appear in
transactions are more important than those that do not appear. As opposed to positive
associations, we call negative associations, associations that negate presence.
