Does not require pre-defining the number of clusters

[taniyabithi]

Agglomerative (Bottom-Up): Starts with each data point as its own cluster and then progressively merges the closest clusters until all data points are in one large cluster.
Divisive (Top-Down): Starts with all data points in one cluster and then recursively divides the cluster into smaller ones until each data point is its own cluster.
Ideal for: Smaller to medium-sized datasets, when yo country email list want to explore the natural hierarchy of segments, and when you don't want to pre-specify the number of clusters.

Customer Segmentation Application:

Niche Market Identification: Uncover smaller, highly specific customer groups that might be overlooked by other methods.
Product Bundle Recommendations: Identify groups of customers who consistently purchase certain products together.
Customer Journey Analysis: Understand how customer behaviors branch out into different paths over time.
Strengths:

Provides a visual representation (dendrogram) of the cluster relationships, aiding interpretation.
Can reveal nested segment structures.
Limitations:

Computationally more intensive for large datasets.
Once a merge or split is made, it cannot be undone.
Determining the optimal number of clusters from the dendrogram can still be subjective.
3. DBSCAN (Density-Based Spatial Clustering of Applications with Noise): Uncovering Irregularly Shaped Clusters
How it works: DBSCAN groups together data points that are closely packed together, marking as outliers (noise) those points that lie alone in low-density regions. It defines clusters based on a minimum number of points within a certain radius.