1,549,391 research outputs found
Evolving temporal association rules with genetic algorithms
A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant - temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to simultaneously search the rule space and temporal space. A methodology for validating the ability of the proposed framework isolates target temporal itemsets in synthetic datasets. The Iterative Rule Learning method successfully discovers these targets in datasets with varying levels of difficulty
Interactive Constrained Association Rule Mining
We investigate ways to support interactive mining sessions, in the setting of
association rule mining. In such sessions, users specify conditions (queries)
on the associations to be generated. Our approach is a combination of the
integration of querying conditions inside the mining phase, and the incremental
querying of already generated associations. We present several concrete
algorithms and compare their performance.Comment: A preliminary report on this work was presented at the Second
International Conference on Knowledge Discovery and Data Mining (DaWaK 2000
Association Mining in Database Machine
Association rule is wildly used in most of the data mining technologies. Apriori algorithm is the fundamental association rule mining algorithm. FP-growth tree algorithm improves the performance by reduce the generation of the frequent item sets. Simplex algorithm is a advanced FP-growth algorithm by using bitmap structure with the simplex concept in geometry. The bitmap structure implementation is particular designed for storing the data in database machines to support parallel computing the association rule mining
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