A metric or a distance measure is an essential concept in clustering because it is used to determine the similarity between objects. However, before applying a distance measure to objects, we have to make a vector of object characteristics; usually, this is a set of numerical values such as human height or weight. Also, some algorithms can work with categorical object features (or characteristics). The standard practice is to normalize feature values. Normalization ensures that each feature gives the same impact in a distance measure calculation. There are many distance measure functions that can be used in the scope of the clustering task. The most popular ones used for numerical properties are Euclidean distance, Squared Euclidean distance, Manhattan distance, and Chebyshev distance. The following subsections describe them in detail.