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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Bui, D.B., Hadzic, F., Potdar, V. (2012). A Framework for Application of Tree-Structured Data Mining to Process Log Analysis. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_52
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DOI: https://doi.org/10.1007/978-3-642-32639-4_52
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