Kokoonanalyysit

Kokoonanalyysit, sometimes referred to 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. The goal is to partition the data into meaningful segments or clusters. This technique is widely applied in various fields, including marketing, biology, social sciences, and pattern recognition.

The similarity between objects is typically measured using a distance metric. Common metrics include Euclidean distance, Manhattan distance, and correlation-based distances. The choice of the distance metric depends on the nature of the data and the problem being addressed.

There are several algorithms for performing cluster analysis, with hierarchical and partitioning methods being two of the most common. Hierarchical clustering builds a tree-like structure of clusters, either by progressively merging smaller clusters into larger ones (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 updating the centroids.

The interpretation of the resulting clusters is a crucial step in the process. Researchers examine the characteristics of the objects within each cluster to understand the nature of the groupings and to draw meaningful conclusions. The number of clusters is often determined by evaluating metrics like the silhouette score or by visual inspection of dendrograms, in the case of hierarchical clustering. Kokoonanalyysit can help identify hidden patterns and structures within complex datasets, leading to better insights and decision-making.