Large Tree Classifier with Heuristic Search and Global Training
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
C4.5: programs for machine learning
C4.5: programs for machine learning
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Two-phase clustering process for outliers detection
Pattern Recognition Letters
Findout: finding outliers in very large datasets
Knowledge and Information Systems
Machine Learning
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Two state-based approaches to program-based anomaly detection
ACSAC '00 Proceedings of the 16th Annual Computer Security Applications Conference
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Workflow mining: a survey of issues and approaches
Data & Knowledge Engineering
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
A Rule-Based Approach for Process Discovery: Dealing with Noise and Imbalance in Process Logs
Data Mining and Knowledge Discovery
Discovering Expressive Process Models by Clustering Log Traces
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Rediscovering workflow models from event-based data using little thumb
Integrated Computer-Aided Engineering
Improving process models by discovering decision points
Information Systems
Intrusion detection using sequences of system calls
Journal of Computer Security
Conformance checking of processes based on monitoring real behavior
Information Systems
Deriving Protocol Models from Imperfect Service Conversation Logs
IEEE Transactions on Knowledge and Data Engineering
ACM Computing Surveys (CSUR)
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
BPM'06 Proceedings of the 4th international conference on Business Process Management
The prom framework: a new era in process mining tool support
ICATPN'05 Proceedings of the 26th international conference on Applications and Theory of Petri Nets
Top-down induction of decision trees classifiers - a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Process diagnostics using trace alignment: Opportunities, issues, and challenges
Information Systems
Information Sciences: an International Journal
Length of stay prediction for clinical treatment process using temporal similarity
Expert Systems with Applications: An International Journal
Reprint of "Length of stay prediction for clinical treatment process using temporal similarity"
Expert Systems with Applications: An International Journal
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A prominent goal of process mining is to build automatically a model explaining all the episodes recorded in the log of some transactional system. Whenever the process to be mined is complex and highly-flexible, however, equipping all the traces with just one model might lead to mixing different usage scenarios, thereby resulting in a spaghetti-like process description. This is, in fact, often circumvented by preliminarily applying clustering methods on the process log in order to identify all its hidden variants. In this paper, two relevant problems that arise in the context of applying such methods are addressed, which have received little attention so far: (i) making the clustering aware of outlier traces, and (ii) finding predictive models for clustering results. The first issue impacts on the effectiveness of clustering algorithms, which can indeed be led to confuse real process variants with exceptional behavior or malfunctions. The second issue instead concerns the opportunity of predicting the behavioral class of future process instances, by taking advantage of context-dependent ''non-structural'' data (e.g., activity executors, parameter values). The paper formalizes and analyzes these two issues and illustrates various mining algorithms to face them. All the algorithms have been implemented and integrated into a system prototype, which has been thoroughly validated over two real-life application scenarios.