# Event Log Preprocessing for Process Mining: A Review
## 3 Research Methodology
### C1. Techniques
#### 3.2.2 Detection-Visualization Techniques  
Detection-visualization techniques aim to identify, group, and isolate those events or traces that can generate problems in the quality of the event log. Within this group, two approaches are identified: clustering and pattern-based techniques. Clustering techniques divide the event log into several subsets, facilitating the understanding and analysis of each member of the subsets. Then, the next step is the identification of noise/anomalous elements within the analyzed subsets. Clustering is one of the techniques most used for data preprocessing in process mining, which has been mostly used for the identification of quality issues associated with noisy values, as well as data diversity. From the formation of similar clusters, it is possible to identify imperfection patterns related to noisy data in the different attributes of the event logs. Several techniques have been proposed in the last decade for trace clustering. They can be divided into three approaches: vector space approaches [34; 35; 36; 37], context aware approaches [38; 39; 40; 41; 42], and model-based approaches [43; 44; 45; 46; 47; 48]. Most of the clustering algorithms aforementioned consider only the event log as input, and use different internal representations for producing the clusters. Traditionally, these algorithms have been applied without taking into consideration the availability of a process model. In contrast, in recent works [49; 50], a different view on traces clustering of an event log is presented. The authors assume that a process model exists and it is used to build simpler groupings of homogeneous traces. Table 3 summarizes the most relevant characteristics of the surveyed works of clustering techniques. \begin{table}
\begin{tabular}{c l l l l l} \hline \hline
**Year** & **Authors** & **Ref** & **Model** & **Approach** & **Algorithms** \\ \hline
2019 & [PERSON] et al. & [50] & Framework for trace clustering of process behavior & Based on generalized alignment & Trace clustering ATC, APOTC, or AMSTC \\ \hline
2019 & [PERSON] and [PERSON] & [37] & Trace clustering using log profiles & Based on trace profiles and missing trace profiles & Self-Organizing Map (SOM) \\ \hline
2017 & [PERSON] et al. & [49] & Technique trace clustering & Based on the concept of multi-alignments, which groups log traces according to & A pseudo-Boolean solver Min- isat+ \\  & & & & & & traces according to \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of event log preprocessing techniques using the clustering approach. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Year** & **Authors** & **Ref** & **Model** & **Approach** & **Algorithms** \\ \hline
2017 & [PERSON] et al. & [42] & Compound trace clustering & Convert the trace clustering problem based on notion of similarity trace into a clustering problem guided by the complexity of the sub-process modes derived from sub-logs & (1) context aware trace clustering technique (GED); (2) sequence clustering technique (STC); (3) flexible heuristic miner (FHM) to discover process models (4) HIF algorithm to locate behavioral patterns recorded in the event log & (1) context aware trace clustering technique  
Within the detection-visualization techniques, some of them perform the preparation of event logs from the pattern identification based on the definition and application of heuristic rules. These rules are identified from observed behaviors or acquired experiences by expert analysts in process mining from the study of different event logs in different domains. Many of the pattern-based techniques state that the event log is not completely correct if a given pattern is not detected in the log [29]. These techniques usually work in conjunction with clustering and abstraction or alignment techniques; thus, allowing the identification of patterns related to noisy data or data diversity. [PERSON] et al.