Klusterianalyysiä
Klusterianalyysiä, often translated as cluster analysis, is a statistical method used to group a set of objects in such a way that objects within the same group (called a cluster) are more similar to each other than to those in other groups. This technique is a form of unsupervised machine learning, meaning it does not require pre-labeled data. The goal is to discover hidden structures or patterns within data by identifying natural groupings.
The process typically begins with a dataset containing observations or data points, each described by a set
Several algorithms exist for performing cluster analysis. K-means clustering is a popular and relatively simple algorithm
Other methods include DBSCAN (Density-Based Spatial Clustering of Applications with Noise), which groups together points that
Klusterianalyysiä has wide-ranging applications across various fields, including market segmentation, image segmentation, anomaly detection, document analysis,