Ryhmittelytutkimus

Ryhmittelytutkimus, also known as cluster analysis, is a statistical method used to group together objects or data points that are similar to each other. The goal is to identify inherent structures or patterns within a dataset by partitioning it into distinct subgroups or clusters. These clusters are formed such that objects within the same cluster are more similar to each other than to objects in other clusters. The concept of similarity is typically defined by a distance or dissimilarity measure, which quantifies how far apart two data points are in a multi-dimensional space.

There are various algorithms for performing cluster analysis, broadly categorized into hierarchical and partitioning methods. Hierarchical methods build a tree-like structure of clusters, either by progressively merging smaller clusters (agglomerative) or by successively splitting larger clusters (divisive). Partitioning methods, such as k-means, aim to divide the data into a pre-specified number of clusters by iteratively assigning data points to the nearest cluster centroid and then recalculating the centroid.

Ryhmittelytutkimus has applications in numerous fields. In marketing, it can be used for customer segmentation to tailor marketing strategies. In biology, it helps in classifying species or identifying gene expression patterns. In image analysis, it can be employed for image segmentation. The choice of algorithm and the definition of similarity are crucial for obtaining meaningful results, and often, domain expertise is required to interpret the generated clusters.