What is 'data mining'?

Data mining refers to the process of discovering patterns and knowledge from large amounts of data. This process involves using various techniques from statistics, machine learning, and artificial intelligence to analyze data sets and identify trends, correlations, and other valuable insights that may not be immediately obvious.

By leveraging algorithms and methods such as clustering, classification, regression, and association rule mining, practitioners can extract meaningful information that can inform decision-making and strategy. The essence of data mining lies in transforming raw data into actionable knowledge, enabling organizations to derive insights that can drive innovation, improve efficiency, and enhance understanding of complex phenomena.

The context of the other options can help clarify this further. Inputting large amounts of data into a database is a fundamental step in data management but does not involve the analysis necessary for mining insights. Cleaning data before analysis is essential for ensuring accurate results but is a preparatory process rather than the discovery of patterns itself. Visualizing data findings is a technique used to present the results of analyses, but it does not encompass the broader definition of data mining, which focuses on the extraction of knowledge from data, rather than merely displaying it.