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The reason data normalization is not considered an algorithm used in MLlib is that it is primarily a data preprocessing technique rather than a machine learning algorithm. In the context of machine learning, algorithms are methods that can learn patterns from the data and make predictions or classifications based on those patterns.

Clustering, regression, and collaborative filtering, on the other hand, are specific algorithms designed to perform tasks in machine learning. Clustering involves grouping data points into clusters based on their features. Regression is used for predicting continuous values based on input features, and collaborative filtering typically refers to methods used for making recommendations based on user-item interactions.

Data normalization is an essential step that prepares and scales data, ensuring that features contribute equally to the learning process, but it does not involve learning from data itself. Therefore, it does not fall under the category of algorithms used in MLlib. Understanding this distinction helps clarify the role of different components in the machine learning workflow.