C4.5: programs for machine learning
C4.5: programs for machine learning
Automating process discovery through event-data analysis
Proceedings of the 17th international conference on Software engineering
Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
An XML Log Standard and Tool for Digital Library Logging Analysis
ECDL '02 Proceedings of the 6th European Conference on Research and Advanced Technology for Digital Libraries
Process Mining: Discovering Direct Successors in Process Logs
DS '02 Proceedings of the 5th International Conference on Discovery Science
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Mining Closed and Maximal Frequent Subtrees from Databases of Labeled Rooted Trees
IEEE Transactions on Knowledge and Data Engineering
Mining and Reasoning on Workflows
IEEE Transactions on Knowledge and Data Engineering
Efficiently Mining Frequent Trees in a Forest: Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Expressive Process Models by Clustering Log Traces
IEEE Transactions on Knowledge and Data Engineering
Frequent Subtree Mining - An Overview
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Rediscovering workflow models from event-based data using little thumb
Integrated Computer-Aided Engineering
Tree model guided candidate generation for mining frequent subtrees from XML documents
ACM Transactions on Knowledge Discovery from Data (TKDD)
Mining Unordered Distance-Constrained Embedded Subtrees
DS '08 Proceedings of the 11th International Conference on Discovery Science
Fuzzy mining: adaptive process simplification based on multi-perspective metrics
BPM'07 Proceedings of the 5th international conference on Business process management
Process mining based on clustering: a quest for precision
BPM'07 Proceedings of the 2007 international conference on Business process management
Process mining as first-order classification learning on logs with negative events
BPM'07 Proceedings of the 2007 international conference on Business process management
NDPMine: efficiently mining discriminative numerical features for pattern-based classification
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Mining of Data with Complex Structures
Mining of Data with Complex Structures
XML documents clustering using a tensor space model
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
BPM'06 Proceedings of the 4th international conference on Business Process Management
A XML-Based workflow event logging mechanism for workflow mining
APWeb'06 Proceedings of the 2006 international conference on Advanced Web and Network Technologies, and Applications
A generic import framework for process event logs
BPM'06 Proceedings of the 2006 international conference on Business Process Management Workshops
XML document clustering using structure-preserving flat representation of XML content and structure
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
A structure preserving flat data format representation for tree-structured data
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
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Process logs are increasingly being represented using XML based templates such as MXML and XES. Popular XML data mining techniques have had limited application to directly mine such data. The majority of work in the process mining field focuses on process discovery and conformance checking tasks often utilizing visualization and simulation based techniques. In this paper, an approach is proposed within which a wider range of data mining methods can be directly applied on tree-structured process log data. Clustering, classification and frequent pattern mining are used as a case in point and experiments are performed on publicly available real-world and synthetic data. The results indicate the great potential of the proposed approach in adding to the available set of methods for process log analysis. It presents an alternative where process model discovery is not the pre-requisite and a variety of methods can be directly applied.