The process of discovering patterns, correlations, anomalies, and insights in large datasets using statistical and computational techniques.
Data mining is exploratory — it searches for patterns that were not known in advance. Techniques include clustering (grouping similar records), classification (assigning records to categories), association rule mining (finding items that frequently appear together), and anomaly detection (identifying unusual records).
Data mining is often a precursor to predictive modelling: you mine the data to discover patterns, then build models to apply those patterns to new data.
Example: A public health agency mines a large dataset of emergency room visits to discover patterns. The analysis reveals that visits for a specific condition cluster geographically near industrial sites and spike in summer months. This pattern — not previously known — prompts further investigation into environmental causes.
Data mining on personal information raises privacy concerns. Any mining of personal data must comply with applicable privacy legislation and ethical guidelines.