clusteringteknikk
Clusteringteknikk refers to a collection of unsupervised machine learning algorithms designed to group similar data points together. The primary goal of clustering is to identify inherent structures within a dataset without prior knowledge of the groupings. This means that the algorithm learns the patterns and relationships in the data itself to form clusters.
The effectiveness of clusteringteknikk relies on the definition of "similarity" or "distance" between data points. Various
Common clusteringteknikk include K-Means, Hierarchical Clustering, and DBSCAN. K-Means aims to partition data into k predefined
Applications of clusteringteknikk are widespread. They are used in customer segmentation for targeted marketing, anomaly detection