## 3 Results
### Comparison of the Simulation Results with Similar Clustering Model Algorithms  
As stated in Section 3.2, there are several clustering methods that can be used for the management of the reliability and performance of the non-intrusive continuous sensor; among them are K-mean clustering, mini-batch K-means clustering, spectral clustering, Gaussian mixture clustering, birch clustering, density-based clustering, hierarchical clustering, and random forest clustering. All of these methods, however, can be successfully implemented to address the reliability and performance evaluation problems. In comparing the simulated results presented when the K-mean clustering model algorithm was implemented, the following model algorithms were applied in this paper to compare the simulated results: the mini-batch K-means clustering model, the spectral clustering model, the Gaussian mixture clustering model, and the birch clustering model algorithms. The simulation is implemented in the online Python 3 platform using the same dataset of 1000 samples obtained from the failure modes (6 features) of the non-intrusive continuous sensors. The simulated results for the different clustering model algorithms when the dataset is grouped into two clusters are presented in Figure 5.  
Figure 5: Simulated results for two clusters showing states of the sensor for the algorithms: mini-batch K-means clustering (**A**), spectral clustering (**B**), Gaussian mixture clustering (**C**), and birch clustering (**D**). From the simulated results (Figure 5), which compare the different clustering model algorithms, it is not difficult to see that the running of the algorithm fits the model correctly, both for the training of the dataset and for the prediction of the cluster in each of the datasets considered. This was virtualized in the results for the six features dataset, which was drawn from the failure modes in the non-intrusive continuous sensor in the heat exchanger system. For the two clusters, a reasonable grouping was found, and it is represented by the colors blue and orange. These colors indicate the fault state (orange region) and non-fault state (blue region) for the mini-batch K-means clustering (A); the fault state (blue region) and non-fault state (orange region) for the Gaussian mixture clustering (C); and finally, the fault state (blue region) and non-fault state (orange region) for the Birch clustering (D). The simulated results generate scatter and dense plots of the dataset, which are separated and identified by the different colors in the cluster. The clusters are generated by the association of the different datasets (observations) using the nearest mean. In Table 2, the simulated results for the clustering model algorithms that were implemented for managing the reliability of the non-intrusive continuous sensor are compared. The results show consistency with the K-means clustering model algorithm, the spectral clustering model algorithm, the Gaussian mixture clustering model algorithm, and the birch clustering model algorithm. In comparing the simulated results presented when the K-mean clustering model algorithm was implemented with three clusters, the following clustering model algorithms were compared: the mini-batch K-means clustering model, the spectral clustering model, the Gaussian mixture clustering model, and the birch clustering model algorithms. The simulation was implemented in the online Python 3 platform using the same dataset of 1000 samples obtained from the failure modes (six features) of the non-intrusive continuous sensors. The simulated results for the different clustering model algorithms when the dataset is grouped into three clusters are presented in Figure 6.  
From the simulated results (Figure 6), which compare the different clustering model algorithms, it is not difficult to see that the running of the algorithm fits the model correctly, both for the training of the dataset and for the prediction of the cluster in each of the datasets considered.