Ryhmittelymenetelmät
Ryhmittelymenetelmät, also known as clustering methods, are unsupervised machine learning techniques used to group similar data points together into clusters. The primary goal is to discover underlying patterns and structures within a dataset without prior knowledge of the group assignments. These methods aim to maximize intra-cluster similarity while minimizing inter-cluster similarity.
Common ryhmittelymenetelmät include k-means, hierarchical clustering, and DBSCAN. K-means is an iterative algorithm that partitions data
Ryhmittelymenetelmiä käytetään laajalti eri aloilla, kuten asiakassegmentoinnissa, kuvankäsittelyssä, tekstianalyysissä ja biologisessa datan analyysissä. Valitun menetelmän tehokkuus