A clustering approach where data points are recursively merged or split to create a tree-like structure (dendrogram).
Hierarchical clustering is a method used to build a hierarchy of clusters, often visualized in a tree-like structure known as a dendrogram. There are two main types of this method: Agglomerative (a bottom-up approach where each observation starts as its own cluster and pairs are iteratively merged based on similarity) and Divisive (a top-down approach where all observations start in one cluster, and splits are performed recursively).
It is categorized as a Hard clustering type. In the clustering summary, it is noted as being used for customer segmentation and time series analysis.
Sources & References
Explore other Task Types terms
B
Binary Classification
Classification task with two possible outcomes (e.g., positive or negative sentiment).
C
Centroid-based Clustering
Organizes data into non-hierarchical clusters based on the arithmetic mean (centroid) of the points. Efficient…
C
Clustering (ML Task)
Grouping data points into clusters based on similarity; an unsupervised learning task.
D
Density-based Clustering
Groups data points based on density and proximity. Does not require pre-defining the number of…
D
Distribution-based Clustering
Assumes data is composed of probabilistic distributions (e.g., Gaussian Mixture Model).
H
Hard Clustering
A type of clustering where data points are assigned exclusively to a single cluster.
M
Multi-Class Classification
Classification where data is assigned exclusively to one of three or more options (e.g., categorizing…
M
Multi-Label Classification
Classification where an input can belong to multiple categories simultaneously (e.g., tagging a blog post…
S
Soft/Fuzzy Clustering
A type of clustering where data points can belong to multiple topics/clusters with varying probabilities…