Définition
Agglomerative clustering starts by treating each object as a separate group. The algorithm then combines the most similar groups step by step until the desired number of clusters or hierarchy is achieved. This approach is useful when it is not known in advance how exactly the data is divided into groups.
Exemple
The analytics service can group users by similar behavior: frequency of visits, interests and purchases.
Pourquoi c'est important
The term is important for customer segmentation, document analysis, searching for similar objects and primary data research without ready-made labels.
Fonctionnement
The algorithm selects a distance measure and a rule for combining clusters. The result is often shown as a tree, showing which groups are close to each other.
Où c'est utilisé
- customer segmentation
- document clustering
- analysis of similar objects
Limites
The method can be slow on large data and sensitive to the choice of distance. The result is not always easy to interpret without verification by an expert.
FAQ
Why is “Agglomerative Clustering” useful to know?
The term is important for customer segmentation, document analysis, searching for similar objects and primary data research without ready-made labels.