Chebyshev distance can be useful when we need to classify two objects as different when they differ only by one of the coordinates. Here is the formula for Chebyshev distance:

The following diagram displays the differences between the various distances:

We can see that Manhattan distance is the sum of the distances in both dimensions, like walking along city blocks. Euclidean distance is just the length of a straight line. Chebyshev distance is a more flexible alternative to Manhattan distance because diagonal moves are also taken into account.

In the current section, we became familiar with the main clustering concept, which is a distance measure. In the following section, we will discuss various types of clustering algorithms.