Pääryhmittely

Pääryhmittely, also known as hierarchical clustering, is a method of cluster analysis which seeks to build a hierarchy of clusters. It is a type of unsupervised learning algorithm used in data mining and machine learning. The goal of pääryhmittely is to group a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups.

The algorithm starts with each object as its own cluster and then iteratively merges the closest pairs of clusters until all objects are in a single cluster or a specified number of clusters is reached. The distance between clusters can be measured using various metrics, such as Euclidean distance, Manhattan distance, or correlation distance.

Pääryhmittely can be represented as a dendrogram, a tree-like diagram that shows the arrangement of the clusters produced by the algorithm. The height of the branches in the dendrogram represents the distance between the clusters being merged.

One of the main advantages of pääryhmittely is that it does not require the number of clusters to be specified in advance. However, it can be sensitive to noise and outliers, and the results can be difficult to interpret, especially for large datasets.

Pääryhmittely is widely used in various fields, such as bioinformatics, image analysis, and market research, to identify patterns and relationships in data. It is also used in exploratory data analysis to gain insights into the structure of the data.