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1. The automated recognition of patterns and regularities in data.
2. Grouping a set of objects in such a way that objects in the same group are more similar to each other than those in other groups.
3. The process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
4. Selecting a subset of relevant features from the original dataset to improve the performance of a machine learning model.
5. Predicting the class or category of an object based on its attributes.
6. Cleaning and transforming raw data into a suitable format before performing analysis or mining.
7. Enabling computers to learn and make decisions from data without being explicitly programmed.
8. Discovering interesting relationships or association patterns among a set of items in large datasets.
9. A flowchart-like structure in which each internal node represents a feature, each branch represents a decision rule, and each leaf node represents the outcome.