Clustmaps are visual tools that uncover hidden patterns and relationships within complex datasets. They organize disparate information into meaningful groups by identifying and grouping similar items. This makes large amounts of data more understandable, providing a starting point for deeper analysis.
The Essence of Clustmaps
Clustmaps group data points based on their inherent similarities. The most common approach is hierarchical clustering, which builds a nested series of clusters.
Hierarchical clustering can be done in two primary ways: agglomerative or divisive. Agglomerative clustering starts with each data point as its own cluster, then merges the most similar clusters until all points form one large cluster. Divisive clustering begins with all data points in one large cluster, then recursively splits the most dissimilar clusters into smaller ones. The result is often a tree-like structure, visually illustrating relationships and groupings at different similarity levels.
Building Blocks of Clustmaps
A clustmap’s effectiveness depends on how similarity or dissimilarity between data points is quantified and how distances between groups are calculated. Distance metrics are mathematical formulas measuring how far apart two data points are in a multi-dimensional space. For numerical data, Euclidean distance is common, calculating the straight-line distance between two points. Different data types, like categorical or text, require alternative metrics. Linkage methods determine how the distance between entire clusters is measured for merging.
Common Linkage Methods
Single linkage: Calculates distance based on the minimum distance between any two points from different clusters, often forming long, chain-like clusters.
Complete linkage: Uses the maximum distance between any two points from different clusters, resulting in more compact, spherical clusters.
Average linkage: Calculates the average distance between all pairs of points across two clusters, balancing single and complete linkage.
Ward’s method: Aims to minimize variance within the newly formed cluster after a merge, often producing clusters of similar sizes and shapes.
The chosen distance metric and linkage method influence the resulting cluster structure and the clustmap’s visual appearance, shaping the insights derived from the data.
Interpreting Clustmap Visuals
The primary visual output of a clustmap is a dendrogram, a tree-like diagram displaying the hierarchy of clusters. In a dendrogram, the horizontal axis represents data points or clusters, while the vertical axis indicates the distance or dissimilarity at which clusters merged. Branches connect individual data points and merge to form larger clusters. The height of horizontal lines where branches merge reveals the dissimilarity level between joined clusters. Taller vertical lines signify less similar merged clusters, while shorter lines indicate more similar clusters.
To identify specific clusters, draw a horizontal line across the dendrogram at a chosen dissimilarity level. Groups of data points connected below this line form distinct clusters. This “cutting” allows users to define a specific number of clusters based on desired granularity. Analyzing the dendrogram helps identify strong hierarchical relationships, discover potential outliers, and recognize inherent structures within the dataset.
Where Clustmaps Shine
Clustmaps are applied across various fields to uncover underlying structures and relationships in complex data. In biology and genomics, they group genes with similar expression patterns or classify species based on genetic or morphological data. Marketing professionals use them to segment customers by purchasing behavior, demographics, or preferences, allowing businesses to tailor strategies. In social sciences, clustmaps analyze survey data to identify groups with similar opinions or characteristics, informing policy or theories. They also serve as a general tool in data analysis, providing an initial step for exploring large datasets and generating hypotheses.