## 1 Introduction  
Predictive monitoring of business processes [1, 2] has emerged in the last 10 years as a discipline of process mining [3] that focuses on creating predictive models of aspects of interest in business processes. Predictive models exploit information available in so-called event logs, i.e., logs of systems supporting the execution of business processes. Event logs contain events, each belonging to a different execution instance (or _case_) of a process. Events record information, such as the time of recording, a label describing the activity that was executed, an id of the (human) resource in charge of execution and possibly other domain specific attributes. In addition to the prediction of the next event in a running case [4] or time-based aspects [5], prediction of the outcome of a running process case [6] is one of the main use cases of business process predictive monitoring. In outcome prediction, an event log contains a categorical label, usually binary, specifying the outcome of cases. This label could be given or reconstructed from other information in an event log. For instance, several publicly available event logs contain labels capturing whether process cases satisfy a given declarative constraint predicated on the order and occurrence of certain activities in a case. The objective is then to use event logs to train a predictive model that accurately predicts the outcome of incomplete (running) process cases. As such, outcome prediction is solved using classification techniques. One of the current main challenges of machine learning predictive models is related to their _interpretability_, that is ([7], p. 1) \"the lack of transparency behind their behaviours, which leaves users with little understanding of how particular decisions are made by these models\". This concern is obviously relevant also in process predictive monitoring, where the output of predictive models may be used by process owners to take important decisions about specific cases, or even to take decisions about process execution automatically. There are fundamentally two ways to approach interpretability of machine learning models [7], i.e., interpretable model extraction and post-hoc explanation. The former implies to generate models that are inherently interpretable, such as rule-based models and, to a lesser extent, decision trees. The latter aims at explaining what type of knowledge has been acquired by models during the training, e.g., explaining the values obtained for weights and biases in a neural network in a human-interpretable way [8]. In this paper, we explore the suitability of association rule mining [9] for supporting outcome-based predictive process monitoring. In particular, first we propose a way to pre-process data to make them suitable for the application of rule-based classifiers. Then, to support the model interpretability, we propose different ways to visualise the rules that constitute a rule-based outcome predictive monitoring model. Specifically, we propose two types of visualisations: (i) a way to globally visualise the features and their values generally determining the value of the outcome label, and (ii) a way to visualise the rules that are activated to determine the outcome predicted for a particular case in an event log. More specifically, from an evaluation standpoint we address the following two problems:  
* To compare the performance of a rule-based classifier against traditional classifiers, such as random forests and gradient boosting machines, commonly used in outcome-based predictive process monitoring. The aim in this case is to assess whether the level of performance achievable by the former can be comparable to the one of the latter. * Exploiting the intrinsic interpretability of rule-based classifiers, to propose visualisations that foster the interpretability of outcome-based process predictive models at the global level, i.e., generally regarding all the traces in an event log, and at the local level, i.e., specifically for a particular trace in an event log.