Pros: Fast, scalable, and relatively easy to understand and implement.

[taniyabithi]

While many clustering algorithms exist, some are more commonly employed and highly effective for customer segmentation:

How it works: This is arguably the most popular and straightforward algorithm. K-Means partitions data into a predetermined number of clusters (K). It iteratively assigns each data point to the nearest cluster centroid (the center of a cluster) and then re-calculates the centroids based on the mean of the assigned points.
Cons: Requires you to specify the number of clusters (K) in country email list advance, sensitive to initial centroid placement, and assumes clusters are spherical and equally sized.
Use Case: Ideal when you have a good idea of how many segments you want to identify, or when dealing with large datasets where computational efficiency is crucial.
Hierarchical Clustering:

How it works: Unlike K-Means, hierarchical clustering doesn't require a predefined number of clusters. It builds a hierarchy of clusters, either by starting with individual data points and progressively merging them (agglomerative) or by starting with one large cluster and recursively splitting it (divisive). The result is a dendrogram, a tree-like diagram that visually represents the relationships between clusters.
Pros: Doesn't require specifying K, provides a visual representation of cluster relationships (dendrogram), and can reveal nested cluster structures.
Cons: Can be computationally intensive for very large datasets, and choosing the optimal number of clusters from the dendrogram can be subjective.
Use Case: Excellent when you're exploring your data and don't know the ideal number of segments, or when you want to understand the hierarchical relationships between customer groups.
DBSCAN (Density-Based Spatial Clustering of Applications with Noise):

How it works: DBSCAN identifies clusters based on the density of data points. It groups together points that are closely packed, marking as outliers those points that lie in low-density regions. It doesn't assume spherical cluster shapes and can effectively identify arbitrarily shaped clusters.
Pros: Can find clusters of arbitrary shapes, robust to outliers (noise), and doesn't require specifying the number of clusters in advance.