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Evaluation of an associative classifier based on position-constrained frequent/closed subtree mining

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Abstract

Tree-structured data are popular in many domains making structural classification an important task. In this paper, an associative classification method is introduced based on a structure preserving flat representation of trees. A major difference to traditional tree mining techniques is that subtrees are constrained by the position in the original trees, leading to a drastic reduction in the number of rules generated, especially with data having great structural variation among tree instances. This characteristic would be desired in the current status of frequent pattern mining, where excessive patterns hinder the practical use of results. However the question remains whether this reduction comes at a high cost in accuracy and coverage rate reduction. We explore this aspect and compare the approach with a state-of-the-art structural classifier based on same subtree type, but not positional constrained in any way. We investigate the effect of using different types of frequent pattern (frequent or closed), or subtree types (induced, embedded or embedded-plus-disconnected subtrees) to the performance of the two classifiers. Different rule strength measures such as confidence, weighted confidence and likelihood are also examined in our study. The experiments on three real-world data sets reveal important similarities and differences between the methods.

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Acknowledgments

We would like to thank Professor Mohammed J. Zaki for making available the XRules classifier and the CSLOG data set. We would also like to acknowledge the constructive comments and advice from Professor George Karypis, which has improved the

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Authors and Affiliations

  1. Department of Computing, Curtin University, Perth, Australia

    Dang Bach Bui, Fedja Hadzic & Michael Hecker

  2. Department of Computer Engineering, Modeling, Electronics, and Systems Science, University of Calabria, Cosenza, Italy

    Andrea Tagarelli

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  1. Dang Bach Bui
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  2. Fedja Hadzic
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  3. Andrea Tagarelli
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  4. Michael Hecker
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Correspondence to Dang Bach Bui.

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Bui, D.B., Hadzic, F., Tagarelli, A. et al. Evaluation of an associative classifier based on position-constrained frequent/closed subtree mining. J Intell Inf Syst 45, 397–421 (2015). https://doi.org/10.1007/s10844-014-0312-9

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  • DOI: https://doi.org/10.1007/s10844-014-0312-9

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