Data Mining

Data mining is a computational process used to uncover patterns, correlations, trends, and anomalies within large sets of data. It involves the use of sophisticated analytical techniques from machine learning, statistics, and database management to extract useful information that may not be readily apparent. The process can include classification, clustering, regression, association rule learning, and anomaly detection among others.

Data mining is widely applied across various domains such as finance for fraud detection, marketing for customer segmentation, healthcare for disease prediction, and e-commerce for recommendation systems. The goal is to transform raw data into actionable insights or knowledge, supporting decision-making processes in business, science, and engineering.

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In retail, data mining is used to analyze customer purchase history to identify buying patterns and trends. This information helps in designing targeted marketing campaigns and recommending products to customers based on their preferences, significantly increasing sales and customer satisfaction.

In healthcare, data mining techniques are employed to analyze patient records and clinical data to predict disease outbreaks, identify risk factors for certain conditions, and improve patient care outcomes by recommending personalized treatment plans.

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