# Anomaly Detection in Automotive Industry Using Clustering Methods--A Case Study
## 5 Conclusions and Future Work  
In the automotive industry, it is widespread to develop new products with similar characteristics designed by different development teams, which increases the complexity of parts immunization abroad for all products. Due to the high volume of data generated by multiple teams, that do not use data mining tools, this investigation can press the production cost by the supply chain increase of complexity. In this context, the present work used clustering tools to recognize the similarity between pieces of real databases \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Algorithm** & **SSE** & **SSW** & **SSB** & **WB** & **CH** & **SI** & **Total** \\ \hline \multirow{3}{*}{**With PCA**} & **K-Medoids** & 3 & 3 & 3 & 3 & 3 & 1 & 16 \\  & **Hierarchical** & 2 & 2 & 2 & 2 & 2 & 3 & 13 \\  & **K-Means** & 1 & 1 & 1 & 1 & 1 & 2 & 7 \\ \hline \multirow{3}{*}{**Without PCA**} & **K-Medoids** & 3 & 3 & 3 & 3 & 3 & 2 & 17 \\  & **Hierarchical** & 2 & 2 & 1 & 1 & 1 & 3 & 10 \\  & **K-Means** & 1 & 1 & 2 & 2 & 2 & 1 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ranking using Borda count method considering results from Hierarchical, K-Means and K-Medoids with and without PCA application. provided by an automotive company. The goal was to optimize the costs of current products to identify opportunities and subsequent cost comparisons. The techniques applied were 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). A ranking based on the Borda count method with the ordered performances indicated that the Hierarchical method stood out, slightly better than the K-Medoids and K-Means, due to a bad performance achieved by K-Medoids regarding SI-index. Despite those being considered classic approaches, they overcame the other methods regarding six clustering metrics. This case study brings important practical contributions to the automotive industry, as it helps to recognize distortions between manufacturing costs, which optimizes the production process and leverages more competitive prices. It also improves the existing knowledge in the literature, helping to improve current techniques for the efficient allocation of automotive parts. One of them is the possibility of developing new methodologies for selecting automotive items with low cost and acceptable efficiency. Despite the many tests performed by this work, the techniques consider only quantitative data, excluding qualitative information, such as item descriptions, NCM code, and engineering class, which is a limitation of the study. Thus, it is necessary to submit the database to models that simultaneously consider quantitative and qualitative information, such as Gower Distance. A future study may be the application of other known techniques of data transformation, such as Box-Cox, to normalize the samples. This may increase the clustering capability of the models. Another possibility is the application of hybrid algorithm compositions that address various clustering techniques, seeking to eliminate the weak points of every single method. This area has shown interesting results in the literature. An example is the PSO-K, a hybrid between the PSO and K-means. The PSO finds an initial solution to reduce the undesirable initialization effects of the K-means. Finally, increasing the number of independent initializations beyond the 30 used in this study may lead the algorithms to better configurations. Conceptualization, M.T.G. and H.V.S. ; methodology, M.T.G., F.T. and H.V.S. ; software, M.T.G. and H.V.S. ; validation, M.T.G., E.M.A.G., J.B., T.A.A., Y.d.S.T., T.M.B., F.T. and H.V.S. ; formal analysis, M.T.G., E.M.A.G., J.B., T.A.A., Y.d.S.T., T.M.B., F.T. and H.V.S. ; investigation, M.T.G. and H.V.S. ; resources, M.T.G. and H.V.S. ; data curation, M.T.G. ; writing--original draft preparation, M.T.G., F.T. and H.V.S.