Review:
Hierarchical Clustering
overall review score: 4.5
⭐⭐⭐⭐⭐
score is between 0 and 5
Hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. It is used in data mining, pattern recognition, image analysis, bioinformatics, and more.
Key Features
- Divisive and agglomerative approaches
- Dendrogram visualization
- No assumptions about the number of clusters
- Suitable for small to medium-sized datasets
Pros
- Flexibility in number of clusters
- Easy interpretation with dendrogram visualization
Cons
- Computationally expensive for large datasets
- Sensitive to noise and outliers