klaszterezési

Klastarozási, often translated as clustering, is a machine learning technique used to group 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 that no pre-labeled data is provided, and the algorithm must discover the inherent structure within the data.

The core idea behind clustering is to identify patterns and similarities within data points. Various algorithms exist, each with its own approach to defining similarity and forming clusters. Common methods include partitioning methods like K-means, hierarchical clustering, density-based methods like DBSCAN, and model-based methods.

K-means is a popular partitioning algorithm that aims to partition n observations into k clusters, where each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. Hierarchical clustering builds a tree of clusters, either by merging smaller clusters (agglomerative) or splitting larger ones (divisive). Density-based clustering identifies clusters as dense regions of data points separated by sparser regions.

Clustering has a wide range of applications across various fields. In marketing, it can be used for customer segmentation to tailor marketing strategies. In biology, it helps in grouping genes with similar functions or classifying species. Image analysis often employs clustering for segmentation or feature extraction. Furthermore, it finds use in anomaly detection, document analysis, and recommendation systems. The choice of clustering algorithm and its parameters often depends on the specific dataset and the desired outcome.