Machine Learning
Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Workflow mining: a survey of issues and approaches
Data & Knowledge Engineering
Combining multiple clustering systems
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Discovering Expressive Process Models by Clustering Log Traces
IEEE Transactions on Knowledge and Data Engineering
Ensembles of Multi-Objective Decision Trees
ECML '07 Proceedings of the 18th European conference on Machine Learning
Cycle Time Prediction: When Will This Case Finally Be Finished?
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems:
Top-down induction of first-order logical decision trees
Artificial Intelligence
Rule Ensembles for Multi-target Regression
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Time prediction based on process mining
Information Systems
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The discovery of predictive models for process performances is an emerging topic, which poses a series of difficulties when considering complex and flexible processes, whose behaviour tend to change over time depending on context factors. We try to face such a situation by proposing a predictive-clustering approach, where different context-related execution scenarios are equipped with separate prediction models. Recent methods for the discovery of both Predictive Clustering Trees and state-aware process performance predictors can be reused in the approach, provided that the input log is preliminary converted into a suitable propositional form, based on the identification of an optimal subset of features for log traces. In order to make the approach more robust and parameter free, we also introduce an ensemble-based clustering method, where multiple PCTs are learnt (using different, randomly selected, subsets of features), and integrated into an overall model. Several tests on real-life logs confirmed the validity of the approach.