What does data mining typically involve?

Data mining primarily focuses on discovering patterns and knowledge from large sets of data. This process involves analyzing data from various perspectives and summarizing it into useful information. The goal is to extract valuable insights that can inform decision-making or predict future trends. Techniques often used in data mining include clustering, classification, association rule mining, and anomaly detection, all aimed at uncovering hidden relationships within the data.

Understanding that data mining is distinct from other data-related tasks helps clarify the focus on finding patterns. For instance, while cleaning and organizing data sets is crucial for ensuring data quality, it is a preparatory step rather than the process of mining itself. Similarly, designing dashboards for data representation is about visualizing data rather than uncovering insights from it. A/B testing is a method for comparing two approaches but does not inherently involve the extraction of patterns from large data sets, which is the essence of data mining. Thus, the choice that highlights discovering patterns directly aligns with the fundamental objective of data mining.