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On the adaptability of G3PARM to the extraction of rare association rules

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Abstract

To date, association rule mining has mainly focused on the discovery of frequent patterns. Nevertheless, it is often interesting to focus on those that do not frequently occur. Existing algorithms for mining this kind of infrequent patterns are mainly based on exhaustive search methods and can be applied only over categorical domains. In a previous work, the use of grammar-guided genetic programming for the discovery of frequent association rules was introduced, showing that this proposal was competitive in terms of scalability, expressiveness, flexibility and the ability to restrict the search space. The goal of this work is to demonstrate that this proposal is also appropriate for the discovery of rare association rules. This approach allows one to obtain solutions within specified time limits and does not require large amounts of memory, as current algorithms do. It also provides mechanisms to discard noise from the rare association rule set by applying four different and specific fitness functions, which are compared and studied in depth. Finally, this approach is compared with other existing algorithms for mining rare association rules, and an analysis of the mined rules is performed. As a result, this approach mines rare rules in a homogeneous and low execution time. The experimental study shows that this proposal obtains a small and accurate set of rules close to the size specified by the data miner.

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Notes

  1. This dataset comprises 392 instances and 8 attributes, and is publicly available for download from the UCI machine learning repository (http://archive.ics.uci.edu/ml/datasets/Auto+MPG).

  2. The dataset used in this example is a real dataset (http://archive.ics.uci.edu/ml/datasets/Zoo) that will be used in the experimental study.

  3. Ankara weather, mushroom, soybean and vote datasets are available for download from the UCI machine learning repository (http://archive.ics.uci.edu/ml/datasets).

  4. The Zoo dataset comprises 102 instances and 17 categorical attributes, and it is publicly available for download from the UCI machine learning repository (http://archive.ics.uci.edu/ml/datasets/Zoo).

  5. The Automobile Performance dataset comprises 392 instances and 8 numerical attributes, and it is publicly available for download from the UCI machine learning repository (http://archive.ics.uci.edu/ml/datasets/Automobile).

  6. All these datasets are publicly available for download from the UCI machine learning repository (http://archive.ics.uci.edu/ml/datasets).

  7. JCLEC is available for download (http://jclec.sourceforge.net).

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Acknowledgments

The authors would like to acknowledge the very helpful comments and suggestions of Dr. Mykola Pechenizkiy (Technical University of Eindhoven) on previous versions of this paper. This work was supported by the Regional Government of Andalusia and the Spanish Ministry of Science and Technology projects, P08-TIC-3720, TIN2008-06681-C06-03 and TIN-2011-22408, respectively, and FEDER funds. This research was also supported by the Spanish Ministry of Education under FPU grant AP2010-0041.

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  1. Department of Computer Science and Numerical Analysis, University of Cordoba, Rabanales Campus, 14071 , Cordoba, Spain

    J. M. Luna, J. R. Romero & S. Ventura

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  1. J. M. Luna
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Correspondence to S. Ventura.

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Luna, J.M., Romero, J.R. & Ventura, S. On the adaptability of G3PARM to the extraction of rare association rules. Knowl Inf Syst 38, 391–418 (2014). https://doi.org/10.1007/s10115-012-0591-9

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