Visualizing the evolution of Web ecologies
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Optimization of constrained frequent set queries with 2-variable constraints
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
DualMiner: a dual-pruning algorithm for itemsets with constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraints
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
On Closed Constrained Frequent Pattern Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
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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.