Klaszterezéssel

Klaszterezéssel, also known 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 a common technique for statistical data analysis used in many fields, including machine learning, data mining, pattern recognition, image analysis, and bioinformatics.

The goal of clustering is to identify natural groupings in the data, where the objects within a cluster are more similar to each other than to those in other clusters. This can be useful for exploratory data analysis, as it can help to uncover hidden patterns and structures in the data.

There are many different clustering algorithms, each with its own strengths and weaknesses. Some of the most common clustering algorithms include k-means clustering, hierarchical clustering, DBSCAN, and Gaussian mixture models. The choice of algorithm depends on the specific characteristics of the data and the goals of the analysis.

Clustering can be used for a variety of purposes, including market segmentation, image compression, anomaly detection, and document retrieval. It is a powerful tool for exploring and understanding complex datasets, and has become an essential technique in the field of data science.