klasterdusmeetod
Klasterdusmeetod, often translated as clustering method, refers to a group of unsupervised machine learning algorithms used to partition a dataset into subsets, or clusters, such that data points within the same cluster are more similar to each other than to those in other clusters. The primary goal is to discover inherent groupings in the data without prior knowledge of these groups.
These methods are widely applied in various fields, including customer segmentation, anomaly detection, image segmentation, and
Popular clustering algorithms include K-means, hierarchical clustering, and DBSCAN. K-means aims to partition data into a
The selection of an appropriate clustering method and its parameters, such as the number of clusters (k)