# Understanding Contrail Business Processes through Hierarchical Clustering: A Multi-Stage Framework
## 2 Literature Review  
In this section, we discuss several trace clustering techniques and their implementations in relation to the process mining. Process mining techniques focus on extracting knowledge from the event data [4]. Considering the complexity and level of divergence in the real-world business environment, process mining has emerged as a solution of several process analytic techniques [6], such as process discovery to explore the process data through modelling and analysing the behaviour of events in the log [7], monitor the process to analyse the conformance of the extracted process data with the models generated as a result of the process discovery and enhance the quality of original process through extracted knowledge [8]. In 2015, [PERSON] et al. proposed a generic framework for process discovery based on Markov Cluster algorithm [6]. Several other techniques, such as Alpha Mining (AM), Heuristic Mining (HM), and Inductive Mining (IM) are investigated in [9] to discover the process model offering the best fit for the Learning Management Systems (LMS) events log. The techniques discussed in [6; 9] provided promising results when dealing with the process data where control flows are either static, or change in the flows have no impact on the business goals. When techniques such as Alpha Mining (AM) and Heuristic Mining (HM) are tested in [9] on the real-world business process data, these algorithms provided unstructured process models. Inductive Miner(IM) is suitable for discovering loops and bottlenecks in the logs but they have not yet tested for large event logs. In this paper, we have used the Heuristic Miner as a tool to discover the process model from the clustered set of process instances. In contrast to [9], we used an extensively large dataset with an average of around 2000 classes per log. This number is 3 times larger then the dataset used in [6; 9]. Trace clustering is used by many researchers to reduce the complexity of the process log by decomposing it into several portions. [PERSON] et al. used presented multiple feature set-based technique in [10] to cluster business process log for achieving trace alignment. Similarly, profiling of similar cases using trace clustering is done through a set of features describing the behaviour of cases in [11]. In 2015, a comprehensive literature review of trace clustering techniques was presented by [PERSON] et al. in [12] in which several approaches for trace clustering were mentioned, such as feature vector representation by activity profile, transition profile, performance profile, maximal repeat feature set and super maximal repeat alphabet feature set. Sequence clustering approaches [13] and tree clustering approaches [14] are the most commonly used techniques for handling a variety of objectives. Active learning inspired ActiTrac [15] improved the performance of clustering by reducing the bias of traditional clustering approaches, increasing the F1 score of resultant process models and reducing the complexity of models measured through fitness metric. ActiTrac performed better result in comparison to seven other clustering techniques but it only supports the single objective trace clustering. In this paper, we compared ActiTrac with our proposed technique and evaluated the results based on the fitness of the identified clusters. In addition to the work performed in [15] we focused on the business perceptive of the clusters and verified the usefulness of the knowledge gained by clustering the raw process log. An adaptive version of ActiTrac is presented in [16] in the form of multi-objective trace clustering using balance fitness, generalisation, precision and simplicity. The authors of [16] used multi-objectives features related to the statistical analysis of the features, but this ignores the underlying business perspective of the business process. Other forms of clustering for process logs are using tree-based clustering and rule-based mining. Association rule mining was initially proposed by [PERSON] et al.