Evolving temporal fuzzy itemsets from quantitative data with a multi-objective evolutionary algorithm

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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Official URL: http://dx.doi.org/10.1109/GEFS.2011.5949497
Link to published version:: https://doi.org/10.1109/GEFS.2011.5949497
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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 - Does NOT include content added after October 2018: Materials and Engineering Research Institute > Centre for Automation and Robotics Research > Mobile Machine and 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: 12 May 2018 16:19
    URI: http://shura.shu.ac.uk/id/eprint/5641

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