Data mining is the process of discovering meaningful patterns, correlations, or anomalies in large datasets using statistical and computational techniques. It sits at the intersection of machine learning, statistics, and database systems. Data mining tasks include classification, clustering, regression, association rule learning (finding frequent itemsets like in market basket analysis), and anomaly detection. The process typically involves data preprocessing, applying algorithms, and interpreting the results (often with domain knowledge). Data mining emerged in the 1990s with the growth of large databases and has applications in business intelligence (customer segmentation, fraud detection), science (genomic pattern discovery), and web analytics. Essentially, it’s about turning raw data into useful information or knowledge.