Klasszterezés
Klasszterezés, often translated as clustering, is a machine learning technique used for grouping a set of objects in such a way that objects in the same group, called a cluster, are more similar to each other than to those in other groups. It is an unsupervised learning method, meaning it does not require labeled data. The primary goal of clustering is to discover hidden patterns or structures within data.
The effectiveness of clustering heavily relies on the definition of similarity or distance between data points.
K-means is an iterative algorithm that partitions data into a predefined number of clusters, denoted by 'k'.
Clustering finds applications in numerous fields. In market research, it can be used to segment customers into