Constraint-based mining of web page associations

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 "highvalue" 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.