A New Self Adaptive Fuzzy Unsupervised Clustering Ensemble Based On Spectral Clustering

Lahmar, Ines; Zaier, Aida; Yahia, Mohamed; Bouallègue, Ridha

doi:10.1109/ssd49366.2020.9364223

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Gupta et al [52] presented a new approach based on evolutionary multi-objective optimization in fuzzy clustering to identify clusters at different levels of fuzziness. Lahmar et al [53] provided a self-adaptive fuzzy c-means method to find the number of clusters; however, scalability to large datasets is not guaranteed in the paper. Shirkhorshidi et al [54] provided an evolving fuzzy-clustering approach, where a small subset of data is clustered in every epoch, centroids are generated, and a global clustering takes place using k-means on these centroids.…”

Section: Clustering Strategiesmentioning

confidence: 99%

Bootstrap–CURE: A Novel Clustering Approach for Sensor Data—An Application to 3D Printing Industry

2022

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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.

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Section: Clustering Strategiesmentioning

confidence: 99%