Data.Mining
Data mining is a crucial step in the process of knowledge discovery in databases
(KDD), which aims at discovering hidden patterns and relationships in a data collection
for prediction and decision-making purposes. Major data mining functions
include characterization, comparison, classification, association, prediction, cluster
analysis, and time-series analysis (Han & Kamber, 2000). Association rule mining
is by far the most frequently used DM technique.
As pointed out by Imielinski and Mannila (1996), a KDD system should offer two
major functionalities: generating KDD objects (i.e., DM output) and retrieving the
ones that were already extracted. This observation comes from the fact that in relational
databases, the output of a query is a table that can be queried later like any
basic table. We fully adhere to this opinion to apply the so-called closure principle
to KDD systems, and we define a set of operations on data mining output.
Association Rule Mining
Mining association rules from a given database of transactions consists to generate all
association rules that have user-specified minimum support and confidence (Agrawal
& Srikant, 1994). Let I={i1, i2,..., im} be a set of m distinct items (e.g., milk, bread).
A transaction T contains a set of items in I, and has an associated unique identifier
2 8 Missaoui, Jatteau, Boujenoui, & Naouali
Copyright ?© 2007, Idea Group Inc.
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