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DATA EVALUATION: CLUSTERING

Table of Content

• DATA EVALUATION: CLUSTERING

• Table of Content

• Why just plot the first two PCA components?

• Defining Customers Groups from Clusters

Why just plot the first two PCA components?

Visualizing data in two dimensions (using the first two PCA components) is a common approach because it allows us to create scatter plots that are easy to interpret and provide insights into the data’s structure.

These two components typically capture the most significant sources of variation in the data. While it’s possible to visualize data in higher dimensions (using more PCA components), it becomes increasingly challenging to create meaningful plots beyond two or three dimensions. Therefore, reducing the dimensionality to two components is a practical choice for visualization.

In case we decide to see how each cluster will be in case we used different pair of principle components

Defining Customers Groups from Clusters

Once you have clustered your customers using Agglomerative Clustering or any other clustering algorithm, you can define customer groups or segments by analyzing the characteristics of each cluster. Here’s how you can approach it:

Approach	Description
Cluster Profiling	Calculate summary statistics for each cluster. For example, compute the mean, median, or mode of various features (e.g., income, purchase behavior) within each cluster. This will provide you with an overview of the typical characteristics of customers in each cluster.
Visual Inspection	Visualize the cluster centers or centroids in the PCA space. You can plot the mean values of the PCA-transformed data for each cluster. This will help you understand how clusters are positioned relative to each other in the reduced-dimensional space.
Feature Importance	If applicable, analyze the feature importances within each cluster. For example, if you used decision tree-based methods, you can examine feature importances to understand which features contribute most to the clustering. This can help you interpret why certain customers are grouped together.
Domain Knowledge	Combine the statistical analysis with domain knowledge to give meaning to the clusters. Domain experts may provide insights into why certain customer segments are formed and what they represent in real-world terms.
Behavioral Patterns	Examine the behavioral patterns or purchasing behaviors of each cluster. Are there clusters that predominantly buy specific types of products? Do certain clusters respond differently to marketing campaigns? Analyzing customer behaviors can help define the groups more precisely.

By combining these approaches, you can gain a deeper understanding of each customer cluster and define them based on their unique characteristics. Keep in mind that the interpretation of clusters should be aligned with your business goals and objectives, and it may require iterative analysis and refinement.