Pisteryhmäanalyysi

Pisteryhmäanalyysi, known in English as cluster analysis, is a multivariate statistical method used to group observations into clusters such that observations within a cluster are more similar to each other than to observations in other clusters. The goal is to identify natural groupings in a dataset without prior knowledge of these groupings.

This technique is exploratory and can be applied to a wide range of data, from biological data like gene expression profiles to market research data for customer segmentation. The similarity between observations is typically measured using a distance metric, such as Euclidean distance, which quantifies the difference between data points in a multi-dimensional space.

There are various algorithms for performing cluster analysis. Hierarchical clustering builds a hierarchy of clusters, either by progressively merging smaller clusters (agglomerative) or by progressively splitting larger clusters (divisive). Partitioning methods, such as k-means, aim to divide the data into a predefined number of clusters, k, by minimizing the within-cluster variance. Density-based methods, like DBSCAN, identify clusters based on regions of high density separated by regions of low density.

The interpretation of the resulting clusters is crucial and often requires domain expertise. Evaluating the quality of the clustering can involve internal validation metrics, which assess the compactness and separation of clusters, or external validation metrics, which compare the clustering results to known class labels. Pisteryhmäanalyysi is a powerful tool for data exploration, pattern discovery, and hypothesis generation.