# Anomaly Detection in Automotive Industry Using Clustering Methods--A Case Study
## 1 Introduction
The new framework is implemented and demonstrated for additive manufacturing, where the similarities of the 3D geometry of parts and printing processes are established by identifying relevant features. In this sense, a challenge for identifying pricing anomalies is to monitor whether the classifications (labels) available in the registration data are adequate for the correct grouping of parts, which should have similar manufacturing costs. In the case study addressed here, two classifications previously existing in the database were evaluated, the NCM and the class number. The NCM, which is an acronym for Mercosur Common Nomenclature, is a hierarchical code of categories. Brazilian taxes, descriptions, and rates are attached to each NCM code [20; 21; 22]. The class number is data created by product engineering when creating new parts. The product engineers freely choose the definition of this number. Consequently, it presents a subjective character, being susceptible to different interpretations. Both labels may show inconsistencies during the sample analysis. In this investigation, we address an actual case study from an automaker. Nowadays, there is an immediate need to expand the company's focus to include analyzing large sample sets, which can be multidimensional and complex for manual analysis. This way, clustering techniques are used to identify pricing anomalies in similar parts, using their specifications as a basis. Clustering methods are tools used on many research fronts, including data mining so that significant knowledge is extracted from apparently unstructured samples [23; 24; 25; 26]. In addition, clustering is a way to group data and identify patterns coherently and unsupervised [27; 28]. Among the different techniques, the literature points out those computational intelligence techniques are an alternative to traditional methods such as K-Means in real problems. However, the current knowledge about this task is not enough to define the best algorithm for all cases [10; 23; 29]. Based on these premises, several clustering approaches are addressed, namely: K-means, K-Medoids, Fuzzy C-means (FCM), Hierarchical, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Self-Organizing Maps (SOM), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). As evaluation and comparison metrics, the following indices are used: Sum of Squared Errors (SSE), Sum of Squares Within Clusters (SSW), Sum of Squares Between Clusters (SSB), Calinski-Harabasz (CH), WB, and Silhouette index. To the best of our knowledge, the use of clustering methods for anomaly detection considering components of an automaker is an unprecedented proposal as a case study. Therefore, this study contributes to an understanding of complex data related to group parts without knowing the classification in advance. It also extends the experience to the existing knowledge through previous research. This paper was organized as follows. Section 2 presents a description of each technique used; Section 3 shows the metrics used for cluster quality evaluation, while Section 4 presents the database, results, and critical analysis. The conclusions are in Section 5.