# data[].attributes.body
No need to predefine the number of clusters.

Produces a dendrogram, allowing flexibility in choosing the number of clusters.

# data[].attributes.body
Simple and efficient for large datasets.

Works well with spherical clusters.

Computationally fast compared to other methods.

# data[].attributes.body
Requires specifying the number of clusters (k).

Struggles with non-spherical clusters and varying cluster sizes.

Sensitive to initial centroid placement and outliers.

# data[].attributes.body
Computationally expensive (O(n²) or O(n³)), making it unsuitable for large datasets.

Sensitive to noise and outliers.

Cannot adjust once a merge or split is done.

# data[].attributes.body
Can find clusters of arbitrary shapes.

Does not require specifying the number of clusters.

Handles noise and outliers well.

# data[].attributes.body
Struggles with varying density clusters.

Performance decreases with high-dimensional data.

Requires careful tuning of parameters (ε, MinPts).

# data[].attributes.body
No need to predefine the number of clusters.

Can detect arbitrarily shaped clusters.

# data[].attributes.body
Computationally expensive for large datasets.

Bandwidth selection is crucial and non-trivial.

# data[].attributes.body
Can model complex cluster shapes using probability distributions.

Provides a probabilistic measure of cluster membership.

# data[].attributes.body
Requires specifying the number of clusters.

Computationally expensive compared to K-Means.

Can converge to a local optimum.

# data[].attributes.body
Works well for non-convex clusters and graph-based data.

Can capture global structure.

# data[].attributes.body
Computationally intensive (depends on eigenvalue decomposition).

Not scalable for large datasets.