Klustermärkning

Klustermärkning, often translated as cluster marking or cluster annotation, is a process in data analysis and machine learning where clusters identified by a clustering algorithm are given meaningful labels or descriptions. After a clustering algorithm groups data points into distinct clusters, these clusters themselves often lack inherent names. Klustermärkning aims to assign a human-understandable label to each cluster, making the results of the clustering analysis more interpretable and actionable.

The process typically involves examining the characteristics of the data points within each cluster. This can be done by analyzing the central tendency of the points in a cluster, such as their mean or median values for various features. Alternatively, key representative data points or "prototypes" of a cluster can be identified and then described. Domain expertise is often crucial in klustermärkning, as a subject matter expert can best interpret the patterns and assign relevant labels based on their knowledge of the data. For example, in customer segmentation, a cluster might be labeled "High-Value Loyal Customers" if the data points within it exhibit high purchasing frequency, large transaction values, and a long history with the company.

Various techniques can be employed for klustermärkning. These include descriptive statistics of cluster features, identifying the most frequent categories within categorical features of a cluster, or using supervised learning models trained on manually labeled data points to predict labels for new clusters. The goal is to bridge the gap between abstract cluster assignments and practical understanding, enabling stakeholders to derive insights and make informed decisions based on the discovered groupings.