Review:
Hierarchical Clustering Algorithms
overall review score: 4.2
⭐⭐⭐⭐⭐
score is between 0 and 5
Hierarchical clustering algorithms are a class of unsupervised machine learning methods used to build a hierarchy of clusters from data points. They operate by either progressively merging smaller clusters into larger ones (agglomerative) or dividing larger clusters into smaller ones (divisive), resulting in a tree-like structure called a dendrogram. These algorithms are widely utilized for exploratory data analysis, pattern recognition, and understanding the inherent structure within datasets.
Key Features
- Creates a hierarchy of clusters represented as a dendrogram
- Can be agglomerative (bottom-up) or divisive (top-down)
- Does not require pre-specifying the number of clusters upfront
- Uses various linkage criteria such as single, complete, average, and Ward's method
- Suitable for small to medium-sized datasets due to computational complexity
- Provides interpretable and visual insights into the data structure
Pros
- Provides a detailed view of data relationships through dendrograms
- Flexible in terms of linkage methods and distance metrics
- Does not require prior knowledge of the number of clusters
- Effective at discovering nested or hierarchical data structures
Cons
- Computationally intensive for large datasets
- Sensitive to noise and outliers, which can affect cluster formation
- Decisions made early in the clustering process are hard to revise later
- Choosing the right linkage method can be challenging and impacts results