Clustering Algorithms Comparison¶

A quick reference guide to the most common clustering algorithms.

Summary¶

Algorithm	Strengths	Weaknesses	Best For
k-Means	Fast, scalable	Must choose \(k\), assumes spherical clusters	Simple baseline, large clean datasets
Hierarchical	Intuitive dendrogram, no \(k\) needed upfront	Slow \(O(N^3)\), doesn't scale well	Small datasets, taxonomy building
DBSCAN	Finds arbitrary shapes, handles noise/outliers	Struggles with varying density, hard to tune eps	Geospatial data, anomaly detection

KSB	Description	How This Addresses It
K4.2	Advanced analytics and ML techniques	Unsupervised learning algorithms for pattern discovery
K4.4	Trade-offs in selecting algorithms	Choosing between clustering approaches based on data characteristics
S1	Scientific methods and hypothesis testing	Validating cluster quality without ground truth labels
S4	Analysis and models to inform outcomes	Using clustering to derive actionable segments
B1	Inquisitive approach	Exploring hidden structure in unlabelled data