Data mining is closely related to machine learning and predictive analytics. Machine learning provides the algorithms and models that enable data mining to identify patterns and make predictions. Predictive analytics uses these patterns to forecast future trends and behaviors, which is essential for strategic planning and decision-making in businesses.

Core techniques in data mining include data classification, clustering, and regression analysis. Data classification sorts data into predefined categories, aiding in the efficient organization and retrieval of information. Clustering groups similar data points together, helping to identify distinct segments within a data set. Regression analysis examines the relationships between variables, enabling predictions about future data points.

Data mining has specific applications such as fraud detection and market basket analysis. In fraud detection, data mining algorithms analyze transaction patterns to identify unusual activities that may indicate fraudulent behavior. Market basket analysis examines customer purchase patterns to identify products that are frequently bought together, helping retailers optimize their inventory and marketing strategies.

Emerging fields like business intelligence and competitive intelligence also benefit from data mining. Business intelligence uses data mining to gather, process, and analyze data for better business decision-making. Competitive intelligence involves analyzing data about competitors to gain a strategic advantage. Both fields rely on the insights generated from data mining to stay ahead in the market.

Supporting technologies such as data warehousing and online databases play a crucial role in data mining. Data warehousing involves storing large amounts of data in a central repository, making it accessible for analysis. Online databases provide the infrastructure needed to manage and query vast datasets efficiently. These technologies ensure that data mining processes are seamless and effective.