How would u define a cluster and an outliner?

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 simultaniously, is one of the most common clustering algorithms.

You can control the algorithm parameters and the visual attributes to suit your needs.

Here is how it works:

Step-by-step explanation:

Hope this helps! :P

A cluster is defined as a group of information (data points and numbers) that are located close together

An Outlier is a number that does not fit in a graph or a data set, like it could be abnormally big or abnormally small.

50=33x+22

step-by-step explanation: