In this paper, we present Fodina, a process discovery technique with a strong focus on robustness and flexibility. To do so, we improve upon and extend an existing process discovery algorithm, namely Heuristics Miner. We have identified several drawbacks which impact the reliability of existing heuristic-based process discovery techniques and therefore propose a new algorithm which is shown to be better performing in terms of process model quality, adds the ability to mine duplicate tasks, and allows for flexible configuration options.

Class imbalance brings significant challenges to customer churn prediction. Many solutions have been developed to address this issue. In this paper, we comprehensively compare the performance of state-of-the-art techniques to deal with class imbalance in the context of churn prediction. A recently developed expected maximum profit criterion is used as one of the main performance measures to offer more insights from the perspective of cost-benefit. The experimental results show that the applied evaluation metric has a great impact on the performance of techniques. An in-depth exploration of reaction patterns to different measures is conducted by intra-family comparison within each solution group and global comparison among the representative techniques from different groups. The results also indicate there is much space to improve solutions' performance in terms of profit-based measure. Our study offers valuable insights for academics and professionals and it also provides a baseline to develop new methods for dealing with class imbalance in churn prediction.

Process mining encompasses the research area which is concerned with knowledge discovery from event logs. One common process mining task focuses on conformance checking, comparing discovered or designed process models with actual real-life behavior as captured in event logs in order to assess the "goodness" of the process model. This paper introduces a novel conformance checking method to measure how well a process model performs in terms of precision and generalization with respect to the actual executions of a process as recorded in an event log. Our approach differs from related work in the sense that we apply the concept of so-called weighted artificial negative events towards conformance checking, leading to more robust results, especially when dealing with less complete event logs that only contain a subset of all possible process execution behavior. In addition, our technique offers a novel way to estimate a process model's ability to generalize. Existing literature has focused mainly on the fitness (recall) and precision (appropriateness) of process models, whereas generalization has been much more difficult to estimate. The described algorithms are implemented in a number of ProM plugins, and a Petri net conformance checking tool was developed to inspect process model conformance in a visual manner.

Process mining encompasses the research area which is concerned with knowledge discovery from information system event logs. Within the process mining research area, two prominent tasks can be discerned. First of all, process discovery deals with the automatic construction of a process model out of an event log. Secondly, conformance checking focuses on the assessment of the quality of a discovered or designed process model in respect to the actual behavior as captured in event logs. Hereto, multiple techniques and metrics have been developed and described in the literature. However, the process mining domain still lacks a comprehensive framework for assessing the goodness of a process model from a quantitative perspective. In this study, we describe the architecture of an extensible framework within ProM, allowing for the consistent, comparative and repeatable calculation of conformance metrics. For the development and assessment of both process discovery as well as conformance techniques, such a framework is considered greatly valuable.

Class imbalance presents significant challenges to customer churn prediction. Many data-level sampling solutions have been developed to deal with this issue. In this paper, we comprehensively compare the performance of several state-of-the-art sampling techniques in the context of churn prediction. A recently developed maximum profit criterion is used as one of the main performance measures to offer more insights from the perspective of cost-benefit. The experimental results show that the impact of sampling methods depends on the used evaluation metric and the impact pattern is interrelated with the classifiers. An in-depth exploration of the reaction patterns is conducted and suitable sampling strategies are recommended for each situation. Furthermore, we also discuss the setting of the sampling rate in the empirical comparison. Our findings will offer a useful guideline for the use of sampling methods in the context of churn prediction.