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tokens: definition of the periphery changes with different norms. Therefore, it is possible to overcome this limitation by adopting a different norm. Consistent with Mitschang et al. ( 2013 ), we use the Manhattan distance, or L 1 norm, as the metric for implementing chemical tagging with DBSCAN. Figure 3 illustrates the two-dimensional projections of the chemical space for our sample and the recovered members according to the DBSCAN algorithm with = 0.4 and K = 6. We found that the recovery rate quickly diminishes when we deviate from these values. As compared to chemical tagging performed with K -means, DBSCAN recovers M13 at a similar rate (23.9%) and 18.4% of M5 members. However, the M13 members are recovered in four DBSCAN groups, compared to just one group for K -means. 3.3. Chemodynamical Clustering Previous sections showed that the process of identifying groups in chemical space can retrieve at most 30% of true members in known globular clusters. This result holds independently of the algorithm adopted to identify clusters in the chemical space. This is likely due to insufficient precisions of chemical abundances. We found that radial velocities of members from distinct globular clusters are almost indistinguishable in several cases. Thus, radial velocity alone is likely not sufficient to recover individual globular clusters either. As the next step, we add the kinematic information, i.e., radial velocity, to the chemical space and propose a new algorithm, SNN. We used AstroPy (Astropy Collaboration et al. 2013 ) and various SciPy packages (Jones et al. 2001 ), including Numpy (Walt et al. 2011 ) and Scikit-learn (Pedregosa et al. 2011 ), to build our algorithm. We applied this algorithm to our sample and identified star clusters in the chemistry–velocity space. All clustering techniques rely on assigning stars based on some distance metric. One problem with picking a distance metric is normalization when two spaces with different ranges are considered (e.g., the chemical space and radial velocity space). One of the main advantages of our algorithm is that it identifies clusters in the chemistry – velocity space by taking the intersection of the nearest neighbors in the chemical space and radial velocity space. This feature avoids normalization between chemical abundances and radial velocity and guarantees that the final nearest neighbors are neighbors in both the chemical and velocity space. The algorithm operates as follows. As suggested in Mitschang et al. ( 2013 ), for each set of elements , the distance in the chemical space between two stars, A and B , is computed as the Manhattan distance, defined below:where X i = [Fe/H], [K/Fe], [S/Fe], [Si/Fe], [Ca/Fe], [Mn/Fe]... The distance between the radial velocities of two stars is a simple Euclidean distance: For every star, a list of nearest neighbors in the chemical space and a list of nearest neighbors in the radial velocity space are respectively found. The neighbors for a particular