Data objects in a given set can be divided into homogeneous classes by clustering.

Clustering or "cluster analysis" is the term for a category of machine learning algorithms that sort data into similar groups.

These groups are called clusters and are determined by the algorithm's assessment of how similar the individual pieces of data are to each other. The algorithm then groups these parts together based on their similarity.

Clustering is a machine learning technique that can be used to group similar data based on their similarity. As an unsupervised algorithm, clustering requires no prior knowledge of the data and works exclusively with similarities within the data itself.

The application of clustering algorithms is very popular and is used for many different purposes, from grouping customers or products to detecting outliers in banking or using it as a spam filter. In this article, we start with a definition of clustering before introducing the different methods and algorithms.

Clustering is a way of organizing data points into groups. This involves looking for similarities in the data, such as age or gender, and identifying groups that are as homogeneous as possible. This means that the members of each group are similar in some way, e.g. that all young men are in this group.

Clustering works without any prior knowledge of which entries are similar, but calculates these similarities based solely on the data itself. This makes it a great method for creating segments or groups without existing knowledge and then deriving knowledge from these segments.

Clustering is a data mining technique used to group similar elements on the basis of a similarity measure. Data groups are found that are more similar to each other than other groups.

There are many goals for the application of clustering. The first category aims to combine similar data points and thus reduce complexity. The other category attempts to identify data points that do not belong to a large group and therefore have special characteristics. This category is referred to as outlier detection. In both categories, the aim is to identify similar groups in order to take appropriate action.

There are many different topics where this insight can be applied. Whether customer clustering, product clustering, as fraud detection or as a spam filter - clustering is a very versatile approach in the field of machine learning and data science.