# Bootstrap-CURE: A Novel Clustering Approach for Sensor Data--An Application to 3D Printing Industry
## 3 State-of-the-Art
### Clustering Strategies  
In general, clustering refers to the task of grouping similar objects. [PERSON] et al. [51] provided a comprehensive survey of various clustering algorithms. While hard-clustering algorithms (e.g., hierarchical and k-means) uniquely assign an object into a single cluster, soft-clustering methods, such as fuzzy-clustering, rather assign each object with a certain membership degree to belong to different clusters. Fuzzy clustering provides a fuzzy partition as a result and requires defuzzification in the post-processing step. [PERSON] et al. [52] presented a new approach based on evolutionary multi-objective optimization in fuzzy clustering to identify clusters at different levels of fuzziness. [PERSON] 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. [PERSON] 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. Although this work also elaborates on the idea of finding previous clusters and making a further clustering over centroids, the use of k-means demands a clear hypothesis on the number of clusters in each epoch, which is not possible in the real 3D printer management application field that the authors are targeting. Among non-fuzzy clustering algorithms, _k-means_ is one of the most popular for its ability to work on large datasets. [PERSON] et al. [55] provided an interesting application of k-means clustering in the characterization of snore signals in the context of upper-airway collapse. [PERSON] et al. [56] proposed the _Lass-weighted k-means_ algorithm specifically useful for a high-dimensional dataset, such as gene-expression data. Another interesting algorithm was developed by [PERSON] et al. [57], using _object-weighting_ in k-means clustering. The algorithm therein can help as a data preprocessing step to deal with outliers, especially in the case of noisy data. This method attempts to increase the weights of the outliers instead of removing them from the study. The results of this algorithm on both simulated and real-life datasets are quite promising too. However, the context of the current research is far from the situation where the real number of clusters to be built is known a priori, and all partitioning methods, including k-means, require the number of clusters to be found as an input parameter. For this reason, the authors will not work with partitioning methods. In some recent works, the use of evolutionary and meta-heuristic optimization is also seen in the context of clustering. [PERSON] et al. [58] used gravitational search to optimize the number of clusters (obtained using _DBSCAN_ approach) in multiple iterations. The final number of clusters is, however, decided manually. The approach seems scalable to large datasets; however, the requirement of running the algorithm in multiple iterations limits the application on cases where near-real-time data are to be analyzed rather quickly, such as the data from 3D printers' sensors. In the current research, the 3D printing sensor data require quicker analysis on a large amount of data, and running multiple iterations may not be acceptable from the practical point of view. [PERSON] et al. [19] proposed another meta-heuristic optimization to improve the _maximum-entropy_ clustering method. The applications of such an algorithm on large-scale data would be interesting to check. In the current age of deep learning, the _graph-convolutional network_ algorithm was provided by [PERSON] et al. [59] for incremental clustering. The algorithm was tested on face images and showed promising results. However, in the context of this paper, the authors deal only with the unsupervised sensor data, and no print images are provided for the analysis.