klusterianalyysillä

Klusterianalyysi, or cluster analysis, is a statistical method used 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. It is an unsupervised learning technique, meaning it does not require pre-labeled data. The primary goal is to discover inherent groupings within the data.

The process typically involves defining a similarity or dissimilarity measure between objects. Common measures include Euclidean distance, Manhattan distance, and cosine similarity, depending on the nature of the data. Once a distance metric is established, various algorithms can be employed to form the clusters.

Popular algorithms include k-means, which aims to partition the data into k clusters where each data point belongs to the cluster with the nearest mean; hierarchical clustering, which builds a tree of clusters either by successively merging smaller clusters (agglomerative) or by successively splitting larger clusters (divisive); and DBSCAN (Density-Based Spatial Clustering of Applications with Noise), which groups together points that are closely packed together, marking as outliers points that lie alone in low-density regions.

The choice of algorithm and the number of clusters are often determined by the specific problem and domain knowledge. Klusterianalyysi has broad applications in fields such as marketing for customer segmentation, biology for gene expression analysis, image processing for image segmentation, and social sciences for identifying social groups. Evaluating the quality of the resulting clusters is crucial and can involve metrics like silhouette scores or examining the interpretability of the formed groups.