Klyngevisning

Klyngevisning, or cluster visualization, refers to the set of visual representations used to display the results of cluster analysis. It helps researchers and analysts understand the structure of the data, assess how well clusters are separated, and interpret which observations belong to which groups.

Common visualization techniques include:

- Dimensionality reduction projections (PCA, t-SNE, UMAP) with points colored by cluster.

- Scatter plots of pairs of variables colored by cluster.

- Dendrograms for hierarchical clustering, showing linkage and cluster splits.

- Heatmaps of means or centroids by cluster.

- Silhouette plots to show the compactness and separation of clusters.

- Parallel coordinates or radar plots to compare variable profiles across clusters.

- Pairwise distance or similarity matrices displayed as heatmaps.

- Multidimensional scaling (MDS) plots.

Considerations in klyngevisning include the potential for artifacts introduced by dimensionality reduction, the influence of the chosen distance metric and data scaling, and the fact that cluster labels are arbitrary. Visualizations should be used to explore and communicate patterns, not to serve as the sole evidence of validity.

Applications span many fields, such as market segmentation, biology (for example, gene expression patterns), social sciences, image segmentation, and consumer analytics. Best practices involve using multiple visualizations to corroborate findings, assessing quantitative measures of cluster quality (like silhouette scores), and validating results across different clustering algorithms or distance metrics.

Common tools for klyngevisning include statistical languages and visualization platforms such as R and Python (with packages for plotting and clustering), as well as general-purpose tools like Tableau or Plotly.