# Bootstrap-CURE: A Novel Clustering Approach for Sensor Data--An Application to 3D Printing Industry
## 8 Conclusions  
In this paper, a new data-science-based intelligent methodology is proposed to assist the management of 3D printers. The work presented in this paper includes three novel contributions. The main contribution of the proposal is a new scalable hierarchical clustering method, called _bootstrap-CURE_, which modifies the first step of the original CURE algorithm by introducing a combination of a bootstrap strategy and a super-classification method so that large datasets can be hierarchically clustered. Apart from scaling up the process to large datasets, the impact on the CPU time reduction is also drastic. This provides a new hierarchical-clustering-based algorithm that suitably scales up to the large sensor data and becomes applicable to additive manufacturing. Hierarchical clustering has many advantages, as it is a non-supervised learning method that does not require human intervention. In particular, the hierarchical clustering family does not require any prior hypotheses for the number of clusters, which is extremely useful in additive manufacturing, as, in real cases, users have no a priori idea of the real number of clusters, and it is much better to leave the algorithm to discover it. Since hierarchical clustering is quadratic, it is not suitable in its original form for large datasets. The proposed _bootstrap-CURE_ method overcomes this limitation and reduces the computational cost drastically by keeping all advantages of the hierarchical clustering. In addition, a second contribution is a novel criterion to automatically determine the number of clusters from the dendrogram in such a way that the results properly approach what clustering experts find by manual inspection. The proposed criterion is based on the analysis of the level indices of the internal nodes of the dendrograms. Being computationally cheap, this method can be introduced in the _bootstrap-CURE_ strategy without incrementing the computational cost significantly. This provides a clustering method that is fully automated and can be used with sensor data coming directly from the 3D printers to identify the operational modes of the machines. Finally, the _bootstrap-CURE_ was introduced in a complete data-science methodology to identify those operational modes by obtaining an automatic interpretation of the clusters' meaning. This provides a very useful decision support tool in the management of 3D printers or complex sensor-equipped machines in general (aero-generators, gas turbines, etc.). The proposal was tested on real data coming from real 3D printers with very promising results. For the future, the integration of the proposed method in an intelligent decision support system that can interpret the operation of an entire fleet of machines in real time is in progress. This will help manufacturers to build better predictive maintenance policies or identify possibilities to improve the 3D printers' design as well. Regarding the proposed algorithm itself, in the current formulation of the _bootstrap-CURE_ approach, the shrinking step (as used in original CURE) is skipped before the integral clustering of all objects since a super-classification process is used with the bootstrap centroids. However, the improvements of implementing the shrinking on top of them will be analyzed in the mid-term. Once the behavior of 3D printers is profiled and interpreted, in the next steps of the research, a reduced subset of significant sensors associated with the different types of failures will be identified, and predictive models will be developed to identify anomalies in advance. Conceptualization: A.K. and K.G. ; methodology: S.S. and K.G. ; validation: S.S. and K.G. and A.K. ; resources: A.K. ; visualization: S.S.; supervision: K.G. All authors have read and agreed to the published version of the manuscript. This work was supported in part by KEMLG-at-IDEAI (UPC) under Grant SGR-2017-574 from the Catalan government. The authors declare no conflict of interest.