Bootstrap-CURE clustering: An investigation of impact of shrinking on clustering performance

Hierarchical clustering is one of the most popular techniques in unsupervised segmentation. However, since it has quadratic complexity as it is based on pairwise distance matrix construction, it tends to be less used with really large data cases. CURE clustering tackles this challenge by accelerating the process through a first hierarchical clustering over a smaller sample from which a set of representative points of resulting clusters is obtained and used to estimate the cluster shape. A KNN process with those representative points allows completing the cluster assignment to the remaining points. This clustering technique scales the hierarchical clustering to large datasets. This work is in continuation of the earlier research, Bootstrap-CURE which uses repeated samples in the first part of the process and gains both robustness and representativeness. Also, the proposed approach uses a criterion for automatic identification of the number of clusters from a dendrogram, so that the bootstrap samples can be automatically processed. In this paper, the concept of shrinkage is proposed as a hyperparameter to the Bootstrap-CURE clustering approach. The inclusion of shrinkage brings the proposed clustering technique closer to the original CURE clustering. The impact of shrinkage on the overall performance of Bootstrap-CURE is further explored. A real-life use case from 3D printers is presented to illustrate the performance of the proposed clustering.Peer ReviewedPostprint (published version

Automatic identification of the number of clusters in hierarchical clustering

Hierarchical clustering is one of the most suitable tools to discover the underlying true structure of a dataset in the case of unsupervised learning where the ground truth is unknown and classical machine learning classifiers are not suitable. In many real applications, it provides a perspective on inner data structure and is preferred to partitional methods. However, determining the resulting number of clusters in hierarchical clustering requires human expertise to deduce this from the dendrogram and this represents a major challenge in making a fully automatic system such as the ones required for decision support in Industry 4.0. This research proposes a general criterion to perform the cut of a dendrogram automatically, by comparing six original criteria based on the Calinski-Harabasz index. The performance of each criterion on 95 real-life dendrograms of different topologies is evaluated against the number of classes proposed by the experts and a winner criterion is determined. This research is framed in a bigger project to build an Intelligent Decision Support system to assess the performance of 3D printers based on sensor data in real-time, although the proposed criteria can be used in other real applications of hierarchical clustering.The methodology is applied to a real-life dataset from the 3D printers and the huge reduction in CPU time is also shown by comparing the CPU time before and after this modification of the entire clustering method. It also reduces the dependability on human-expert to provide the number of clusters by inspecting the dendrogram. Further, such a process allows applying hierarchical clustering in an automatic mode in real-life industrial applications and allows the continuous monitoring of real 3D printers in production, and helps in building an Intelligent Decision Support System to detect operational modes, anomalies, and other behavioral patterns.Peer ReviewedPostprint (author's final draft

Bootstrap–CURE: A novel clustering approach for sensor data: an application to 3D printing industry

The agenda of Industry 4.0 highlights smart manufacturing by making machines smart enough to make data-driven decisions. Large-scale 3D printers, being one of the important pillars in Industry 4.0, are equipped with smart sensors to continuously monitor print processes and make automated decisions. One of the biggest challenges in decision autonomy is to consume data quickly along the process and extract knowledge from the printer, suitable for improving the printing process. This paper presents the innovative unsupervised learning approach, bootstrap–CURE, to decode the sensor patterns and operation modes of 3D printers by analyzing multivariate sensor data. An automatic technique to detect the suitable number of clusters using the dendrogram is developed. The proposed methodology is scalable and significantly reduces computational cost as compared to classical CURE. A distinct combination of the 3D printer’s sensors is found, and its impact on the printing process is also discussed. A real application is presented to illustrate the performance and usefulness of the proposal. In addition, a new state of the art for sensor data analysis is presented.This work was supported in part by KEMLG-at-IDEAI (UPC) under Grant SGR-2017-574 from the Catalan government.Peer ReviewedPostprint (published version

Towards expert-inspired automatic criterion to cut a dendrogram for real-industrial applications

Hierarchical clustering is one of the most preferred choices to understand the underlying structure of a dataset and defining typologies, with multiple applications in real life. Among the existing clustering algorithms, the hierarchical family is one of the most popular, as it permits to understand the inner structure of the dataset and find the number of clusters as an output, unlike popular methods, like k-means. One can adjust the granularity of final clustering to the goals of the analysis themselves. The number of clusters in a hierarchical method relies on the analysis of the resulting dendrogram itself. Experts have criteria to visually inspect the dendrogram and determine the number of clusters. Finding automatic criteria to imitate experts in this task is still an open problem. But, dependence on the expert to cut the tree represents a limitation in real applications like the fields industry 4.0 and additive manufacturing. This paper analyses several cluster validity indexes in the context of determining the suitable number of clusters in hierarchical clustering. A new Cluster Validity Index (CVI) is proposed such that it properly catches the implicit criteria used by experts when analyzing dendrograms. The proposal has been applied on a range of datasets and validated against experts ground-truth overcoming the results obtained by the State of the Art and also significantly reduces the computational cost .Peer ReviewedPostprint (published version

Direct Measurement of Ballistic and Diffusive Electron Transport in Gold

We experimentally show that the ballistic length of hot electrons in laser-heated gold films can exceed ∼150 nm, which is ∼50% greater than the previously reported value of 100 nm inferred from pump–probe experiments. We also find that the mean free path of electrons at the peak temperature following interband excitation can reach upward of ∼45 nm, which is higher than the average value of 30 nm predicted from our parameter-free density functional perturbation theory. Our first-principles calculations of electron–phonon coupling reveal that the increase in the mean free path due to interband excitation is a consequence of drastically reduced electron–phonon coupling from lattice stiffening, thus providing the microscopic understanding of our experimental findings