Locating regions of high density via DBSCAN
Although we can't cover the vast amount of different clustering algorithms in this chapter, let's at least include one more approach to clustering: density-based spatial clustering of applications with noise (DBSCAN), which does not make assumptions about spherical clusters like k-means, nor does it partition the dataset into hierarchies that require a manual cut-off point. As its name implies, density-based clustering assigns cluster labels based on dense regions of points. In DBSCAN, the notion of density is defined as the number of points within a specified radius, 
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According to the DBSCAN algorithm, a special label is assigned to each example (data point) using the following criteria:
