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. Various metrics can be employed, such as Euclidean distance, Manhattan distance, or cosine similarity, depending on the nature of the data. Different algorithms exist to perform clustering, each with its own strengths and weaknesses. Common examples include K-means, hierarchical clustering, DBSCAN, and Gaussian Mixture Models.

K-means is an iterative algorithm that partitions data into a predefined number of clusters, denoted by 'k'. It aims to minimize the within-cluster sum of squares. Hierarchical clustering, on the other hand, builds a hierarchy of clusters, either in a bottom-up (agglomerative) or top-down (divisive) manner. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) identifies clusters based on the density of data points, making it effective at finding arbitrarily shaped clusters and identifying outliers.

Clustering finds applications in numerous fields. In market research, it can be used to segment customers into distinct groups for targeted marketing campaigns. In image analysis, it helps in image segmentation and object recognition. Bioinformatics uses clustering to group genes with similar expression patterns or to classify proteins. Document analysis can benefit from clustering to group similar documents for easier retrieval and organization. The choice of clustering algorithm and distance metric often depends on the specific problem and the characteristics of the dataset.