Simple to understand and implement

[taniyabithi]

K-Means Clustering: The Go-To for Simplicity and Speed
How it works: K-Means is perhaps the most popular and intuitive clustering algorithm. You specify the number of clusters you want (k), and the algorithm iteratively assigns each data point to the cluster whose centroid (mean) is closest. It then re-calculates the centroids based on the new assignments until convergence.

Ideal for: Large datasets, when you have a reasonable idea of the number of segments you expect, and for quick initial segmentation.

Customer Segmentation Application:

RFM (Recency, Frequency, Monetary) Segmentation: Group country email list customers based on how recently they purchased, how often they purchase, and how much they spend.
Behavioral Segmentation: Cluster customers based on their website activity (pages visited, time spent, clicks), app usage, or email engagement.
Demographic Segmentation: While less common for K-Means alone, it can be used to group customers by age, income, location, etc., if combined with other variables.
Strengths:

Computationally efficient for large datasets.
Produces well-separated clusters.
Limitations:

Requires pre-defining the number of clusters (k), which can be challenging.
Sensitive to initial centroid placement (can lead to different results).
Assumes clusters are spherical and equally sized.
Sensitive to outliers.
Hierarchical Clustering: Building a Tree of Segments
How it works: Hierarchical clustering builds a hierarchy of clusters, represented as a dendrogram (a tree-like diagram). There are two main.