Dynamic Student Performance Clustering with Machine Learning

Design a Python script using scikit-learn that automatically clusters student performance data across multiple subjects, identifying hidden learning patterns. The algorithm should handle datasets with missing values, normalize scores, and generate interpretable cluster centroids. Include visualization using seaborn that highlights each cluster's characteristics, with specific emphasis on identifying potential intervention strategies for different student groups.