# Clustering-Based Predictive Process Monitoring

arXiv: https://arxiv.org/abs/1506.01428 · HTML: https://mlsanity.com/paper/1506.01428/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2015.

## Built on by (most cited first, 24 of 24)

- [Counterfactual Explanations for Predictive Business Process Monitoring](https://mlsanity.com/paper/2202.12018/) (2022)
- [When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints](https://mlsanity.com/paper/2206.07745/) (2022)
- [How do I update my model? On the resilience of Predictive Process Monitoring models to change](https://mlsanity.com/paper/2109.03501/) (2021)
- [Trace Encoding in Process Mining: a survey and benchmarking](https://mlsanity.com/paper/2301.02167/) (2023)
- [Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models](https://mlsanity.com/paper/2203.16073/) (2022)
- [Predictive Compliance Monitoring in Process-Aware Information Systems: State of the Art, Functionalities, Research Directions](https://mlsanity.com/paper/2205.05446/) (2022)
- [Machine learning in business process management: A systematic literature review](https://mlsanity.com/paper/2405.16396/) (2024)
- [Achieving Group Fairness through Independence in Predictive Process Monitoring](https://mlsanity.com/paper/2412.04914/) (2024)
- [Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring](https://mlsanity.com/paper/2506.03696/) (2025)
- [Automatically Reconciling the Trade-off between Prediction Accuracy and Earliness in Prescriptive Business Process Monitoring](https://mlsanity.com/paper/2307.05939/) (2023)
- [Explainable Predictive Process Monitoring: A User Evaluation](https://mlsanity.com/paper/2202.07760/) (2022)
- [Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?](https://mlsanity.com/paper/2202.08041/) (2022)
- [Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring](https://mlsanity.com/paper/2403.11642/) (2024)
- [Clustering Object-Centric Event Logs](https://mlsanity.com/paper/2207.12764/) (2022)
- [A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas](https://mlsanity.com/paper/2302.04018/) (2023)
- [Time-Aware and Transition-Semantic Graph Neural Networks for Interpretable Predictive Business Process Monitoring](https://mlsanity.com/paper/2508.09527/) (2025)
- [Enhancing Stochastic Petri Net-based Remaining Time Prediction using k-Nearest Neighbors](https://mlsanity.com/paper/2206.13109/) (2022)
- [Nirdizati: an Advanced Predictive Process Monitoring Toolkit](https://mlsanity.com/paper/2210.09688/) (2022)
- [Beyond Yes or No: Predictive Compliance Monitoring Approaches for Quantifying the Magnitude of Compliance Violations](https://mlsanity.com/paper/2502.01141/) (2025)
- [Clustering-based Aggregations for Prediction in Event Streams](https://mlsanity.com/paper/2210.09738/) (2022)
- [Encoder-Decoder Model for Suffix Prediction in Predictive Monitoring](https://mlsanity.com/paper/2211.16106/) (2022)
- [Explain, Adapt and Retrain: How to improve the accuracy of a PPM classifier through different explanation styles](https://mlsanity.com/paper/2303.14939/) (2023)
- [A Review of AI and Machine Learning Contribution in Predictive Business Process Management (Process Enhancement and Process Improvement Approaches)](https://mlsanity.com/paper/2407.11043/) (2024)
- [EHHN: An Event-driven Heterogeneous Hypergraph Network for Object-Centric Next Activity Prediction](https://mlsanity.com/paper/2607.01785/) (2026)

## Abstract (arXiv)

Business process enactment is generally supported by information systems that record data about process executions, which can be extracted as event logs. Predictive process monitoring is concerned with exploiting such event logs to predict how running (uncompleted) cases will unfold up to their completion. In this paper, we propose a predictive process monitoring framework for estimating the probability that a given predicate will be fulfilled upon completion of a running case. The predicate can be, for example, a temporal logic constraint or a time constraint, or any predicate that can be evaluated over a completed trace. The framework takes into account both the sequence of events observed in the current trace, as well as data attributes associated to these events. The prediction problem is approached in two phases. First, prefixes of previous traces are clustered according to control flow information. Secondly, a classifier is built for each cluster using event data to discriminate between fulfillments and violations. At runtime, a prediction is made on a running case by mapping it to a cluster and applying the corresponding classifier. The framework has been implemented in the ProM toolset and validated on a log pertaining to the treatment of cancer patients in a large hospital.