Mining Generalized Association Rules in an Evolving Environment

Mining Generalized Association Rules in an Evolving Environment

Wen-Yang Lin (National University of Kaohsiung, Taiwan) and Ming-Cheng Tseng (Institute of Information Engineering, Taiwan)
Copyright: © 2009 |Pages: 7
DOI: 10.4018/978-1-60566-010-3.ch197
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The mining of Generalized Association Rules (GARs) from a large transactional database in the presence of item taxonomy has been recognized as an important model for data mining. Most previous studies on mining generalized association rules, however, were conducted on the assumption of a static environment, i.e., static data source and static item taxonomy, disregarding the fact that the taxonomy might be updated as new transactions are added into the database over time, and as such, the analysts may have to continuously change the support and confidence constraints, or to adjust the taxonomies from different viewpoints to discover more informative rules. In this chapter, we consider the problem of mining generalized association rules in such a dynamic environment. We survey different strategies incorporating state-of-the-art techniques for dealing with this problem and investigate how to efficiently update the discovered association rules when there are transaction updates to the database along with item taxonomy evolution and refinement of support constraint.
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An association rule is an expression of the form XY, where X and Y are sets of items. Such a rule reveals that transactions in the database containing items in X tend to also contain items in Y, and the probability, measured as the fraction of transactions containing X that also contain Y, is called the confidence of the rule. The support of the rule is the fraction of the transactions that contain all items in both X and Y. The problem of mining association rules is to discover all association rules that satisfy support and confidence constraints.

In many applications, there are explicit or implicit taxonomies over the items, so it may be more useful to find associations at different taxonomic levels than only at the primitive concept level. For example, consider the taxonomy of items in Figure 1. It is likely that the association rule,

Figure 1.

Example of taxonomy

Systemax V ⇒ HP LaserJet (sup = 20%, conf =100%) does not hold when the minimum support is set to 25%, but the following association rule may be valid,Desktop PC ⇒ HP LaserJet

This kind of association rule with taxonomy is also called the generalized association rule (Srikant & Agrawal, 1995) or the multi-level association rule (Han & Fu, 1995). The work in Srikant and Agrawal (1995) aimed at finding associations among items at any level of the taxonomy, whereas the objective in Han and Fu (1995) was to discover associations level-by-level in a fixed hierarchy, i.e., only associations among items on the same level were examined progressively from the top level to the bottom.

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