DBSCAN: Part 2 

Hello and welcome. In this video, we'll be covering DB scan. A density-based clustering algorithm which is appropriate to use when examining spatial data. So let's get started. Most of the traditional clustering techniques such as K-Means, hierarchical, and Fuzzy clustering can be used to group data in an unsupervised way. However, when applied to tasks with arbitrary shaped clusters or clusters within clusters, traditional techniques might not be able to achieve good results that is, elements in the same cluster might not share enough similarity or the performance may be poor. Additionally, while partitioning based algorithms such asK-Means may be easy to understand and implement in practice, the algorithm has no notion of outliers that is, all points are assigned to a cluster even if they do not belong in any. In the domain of anomaly detection, this causes problems as anomalous points will be assigned to the same cluster as normal data points. The anomalous points pull the cluster centroid towards them making it harder to classify them as anomalous points. In contrast, density-based clustering locates regions ofhigh density that are separated from one another by regions of low density. Density in this context is defined as the number of points within a specified radius.A specific and very popular type of density-based clustering is DBSCAN.DBSCAN is particularly effective for taskslike class identification on a spatial context.The wonderful attributes of the DBSCAN algorithm is that it canfind out any arbitrary shaped cluster without getting effected by noise.