Question

What are some advantages and disadvantages of Hierarchical Clustering compared to K-Means Clustering?

Answer 1

Hierarchical Clustering advantages include not requiring the number of clusters to be specified in advance, providing a hierarchy of clusters, and handling non-linearly separable data. Disadvantages include sensitivity to outliers, computationally expensive distance calculations, and producing unbalanced clusters.

K-Means Clustering advantages include computational speed, easy determination of the number of clusters, and generating balanced, convex clusters. Disadvantages include requiring the number of clusters to be specified, sensitivity to initial centroid selection, and assuming clusters are circular/elliptical and of similar sizes.

Step-by-step explanation:

Hierarchical Clustering:

• Advantages:

1. Does not require the number of clusters to be specified in advance
2. Provides a hierarchy of clusters, allowing for exploration at different levels of granularity
3. Can handle non-linearly separable data

Disadvantages:

1. Can be sensitive to outliers
2. Requires computationally expensive distance calculations for large datasets
3. Can produce unbalanced clusters if the data has varying densities

K-Means Clustering:

• Advantages:

1. Can be computationally faster than hierarchical clustering, especially for large datasets
2. Allows for easy determination of the number of clusters
3. Generates balanced, convex clusters

Disadvantages:

1. Requires the number of clusters to be specified in advance
2. Can be sensitive to the initial selection of cluster centroids
3. Assumes clusters are circular/elliptical in shape and of similar sizes