# Clustering Moving Object Trajectories: Integration in CROSS-CPP Analytic Toolbox
## 6 Discussion and Conclusions  
In this paper, we have presented results of the integration Quickbundles algorithm for the moving objects' trajectory clustering in the CROSS-CPP platform. Two main aspects must be discussed: (i) integration issues and (ii) clustering algorithm performance. In relation to integration, the CROSS-CPP marketplace makes it possible for data providers to store data so that service providers can use these data to generate applications based on the knowledge extracted from these data. In order to extract the knowledge from the data, the analytic toolbox has been developed and integrated. One of the main advantages of the analytic toolbox is its flexibility to include new functionalities; in particular, in this paper, we have shown how clustering for moving objects has been integrated. More precisely, the Quickbundles algorithm has been adapted from the health domain to calculate the clustering of trajectories. In the paper we have shown the end-to-end process (see Section 5) from data harvesting to data analysis. In relation to performance, the Quickbundles algorithm has been proposed in this paper for trajectory clustering. We chose this instead of center-based clustering algorithms such as K-means due to the fact that spherical clusters are not appropriate for trajectory clustering. As a consequence, we conducted experiments to compare the behavior of spectral clustering and Quickbundles using different distance measures. The obtained results show that Quickbundles outperforms spectral clustering, with this behavior being coherent in both datasets: the Porto Kaggle and the CROSS-CPP data. The silhouette metric was used to compare results. The silhouette coefficient metrics are known to be a ratio that depends on both the cohesion, that is to say the similarity inside each cluster, and on the separation, that is, the distance of points belonging to different clusters. From analyzing the obtained results, one could conclude that QB algorithm obtain clusters with a better ratio between cohesion and separation. Spectral clustering may yield clusters with better cohesion or separation metrics, but not both at the same time. However, it would be a future work to analyze these metrics in isolation. In relation to experimentation, not all of the possible parameters for the compared algorithms have been explored. In particular, for spectral only, the number clusters has been modified in the different executions. For the case of QB, we ran experiments adjusting both the resampling factor and the neighboring threshold. In particular, for QB the experiments raised the fact that the resampling factor does not offer great changes in performance but the neighbouring threshold was shown to be determinant. This is in accordance with what has been already mentioned in the literature. For experiments run with spectral clustering, we chose the number of clusters yielding the best results. Future work should include a deeper study by using different methods to construct the affinity matrix and fine-tuning the C and \(\sigma\) parameters, among others. In relation with the distance measure, despite the fact that not high differences in silhouette coefficient were observed, results yielded slightly better results when using the geodesic WS84 in both datasets. Note that this distance improved performance even in the case of Porto, where the difference of distances is not very big. Regarding time consumption, although experiments were not conducted to measure this issue, we could observe that the spectral algorithm took up to three times less the time to execute in comparison to Quickbundles. Although this should be further explored, results obtained are in accordance to literature: in [13], the authors establish that the time of execution of QB increases linearly with the number of trajectories. Finally, the clustering was performed in batch mode. However, it is important to note that the CROSS-CPP analytic toolbox allows for data-streaming mode.