MATTHEWS, Stephen G., GONGORA, Mario A. and HOPGOOD, Adrian A. (2011). Evolving temporal fuzzy itemsets from quantitative data with a multi-objective evolutionary algorithm. In: IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS), 2011. IEEE Xplore, 9-16.
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We present a novel method for mining itemsets that are both quantitative and temporal, for association rule mining, using multi-objective evolutionary search and optimisation. This method successfully identifies temporal itemsets that occur more frequently in areas of a dataset with specific quantitative values represented with fuzzy sets. Current approaches preprocess data which can often lead to a loss of information. The novelty of this research lies in exploring the composition of quantitative and temporal fuzzy itemsets and the approach of using a multi-objective evolutionary algorithm. This preliminary work presents the problem, a novel approach and promising results that will lead to future work. Results show the ability of NSGA-II to evolve target itemsets that have been augmented into synthetic datasets. Itemsets with different levels of support have been augmented to demonstrate this approach with varying difficulties.
|Item Type:||Book Section|
|Research Institute, Centre or Group:||Materials and Engineering Research Institute > Centre for Robotics and Automation > Mobile Machine and Vision Laboratory|
|Depositing User:||Adrian Hopgood|
|Date Deposited:||31 Aug 2012 15:05|
|Last Modified:||17 Sep 2012 15:14|
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