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Everyone is trying to make sense of their data. In the real world, data is often not easy to separate, and patterns are not usually obvious. Clustering helps you find similarity groups in your data and it is one of the most common tasks in the Data Science. Finding the “outliers”, which are the observations in your data isolated from the rest of observations, is often a non-easy analytics task by its own. It explains why the density-based clustering, which find similarity groups and outliers in your data simultaneously, is one of the most common clustering algorithms.