Abstract
The use of association rule mining carries the attendant challenge
of focusing on appropriate data subsets so as to reduce the volume
of association rules produced. The intent is to heuristically identify
�interesting� rules more efficiently, from less data. This challenge
is similar to that of identifying �high-value� attributes within
the more general framework of machine learning, where early identification
of key attributes can profoundly influence the learning outcome.
In developing heuristics for improving the focus of association rule
mining, there is also the question of where in the overall process
such heuristics are applied. For example, many such focusing methods
have been applied after the generation of a large number of rules,
providing a kind of ranking or filtering. An alternative is to constrain
the input data earlier in the data mining process, in an attempt
to deploy heuristics in advance, and hope that early resource savings
provide similar or even better mining results. In this paper we consider
possible improvements to the problem of achieving focus in web mining,
by investigating both the articulation and deployment of rule constraints
to help attain analysis convergence and reduce computational resource
requirements.
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