Choosing the Optimal K

Requirements Clarification & Assessment

1. Understanding the Problem:

   • The goal is to determine the optimal number of clusters (k) in k-means clustering.
   • Clusters should be meaningful and provide insights into the data structure.

2. Data Characteristics:

   • Assess the nature and dimensionality of the dataset.
   • Understand the distribution and variance within the data.

3. Objective:

   • Achieve a balance between minimizing within-cluster variance and maximizing between-cluster variance.
   • Avoid overfitting or underfitting by selecting an appropriate k.

4. Constraints:

   • Computational resources and time constraints for running multiple clustering iterations.
   • The interpretability of the resulting clusters in the context of the problem domain.

5. Evaluation Metrics:

   • Define metrics for evaluating clustering quality, such as the Elbow Method, Silhouette Score, etc.