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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Abstract
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 |
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Research Institute, Centre or Group - Does NOT include content added after October 2018: | Materials and Engineering Research Institute > Modelling Research Centre > Microsystems and Machine Vision Laboratory |
Identification Number: | https://doi.org/10.1109/GEFS.2011.5949497 |
Page Range: | 9-16 |
Depositing User: | Adrian Hopgood |
Date Deposited: | 31 Aug 2012 14:05 |
Last Modified: | 18 Mar 2021 13:49 |
URI: | https://shura.shu.ac.uk/id/eprint/5641 |
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