A framework for application of tree-structured data mining to process log analysis
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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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Tan, H.; Hadzic, Fedja; Dillon, T. (2012)The increasing need for representing information through more complex structures where semantics and relationships among data objects can be more easily expressed has resulted in many semi-structured data sources. Structure ...
Hadzic, Fedja; Tan, H.; Dillon, Tharam S.; Chang, Elizabeth (2007)Frequent subtree mining has found many useful applications in areas where the domain knowledge is presented in a tree structured form, such as bioinformatics, web mining, scientific knowledge management etc. It involves ...
Chang, Elizabeth; Tan, H.; Dillon, Tharam S.; Feng, L.; Hadzic, Fedja (2005)An XML enabled framework for representation of association rules in databases was first presented in [Feng03]. In Frequent Structure Mining (FSM), there are techniques proposed to mine frequent patterns from complex trees ...