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    A framework for application of tree-structured data mining to process log analysis

    Access Status
    Fulltext not available
    Authors
    Bui, Dang
    Hadzic, Fedja
    Potdar, Vidyasagar
    Date
    2012
    Type
    Conference Paper
    
    Metadata
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    Citation
    Bui, D. and Hadzic, F. and Potdar, V. 2012. A framework for application of tree-structured data mining to process log analysis, in Hujin, Y. and Costa, J.A.F. and Barreto, G. (ed), Proceedings of The 13th International Conference on Intelligent Data Engineering and Automated Learning, Aug 29-31 2012, pp. 423-434. Natal, Brazil: Springer Verlag.
    Source Title
    Intelligent Data Engineering and Automated Learning 2012 - IDEAL 2012 13th International Conference Proceedings
    Source Conference
    The 13th International Conference on Intelligent Data Engineering and Automated Learning
    DOI
    10.1007/978-3-642-32639-4_52
    ISBN
    978-3-642-32639-4
    URI
    http://hdl.handle.net/20.500.11937/19696
    Collection
    • Curtin Research Publications
    Abstract

    Many data mining and simulation based algorithms have been applied in the process mining field; nevertheless they mainly focus on the process discovery and conformance checking tasks. Even though the event logs are increasingly represented in semi-structured format using XML-based templates, commonly used XML mining techniques have not been explored. In this paper, we investigate the application of tree mining techniques and propose a general framework, within which a wider range of structure aware data mining techniques can be applied. Decision tree learning and frequent pattern mining are used as a case in point in the experiments on publicly available real dataset. The results indicate the promising properties of the proposed framework in adding to the available set of tools for process log analysis by enabling (i) direct data mining of tree-structured process logs (ii) extraction of informative knowledge patterns and (iii) frequent pattern mining at lower minimum support thresholds.

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