Data Mining
Data mining is the process of discovering useful, non-obvious patterns in large volumes of data, at the intersection of statistics, machine learning and databases. We explain its relation to the KDD process, its main tasks, the CRISP-DM methodology, its difference from neighboring terms and its privacy considerations.
Data mining is the process of discovering useful, non-obvious patterns, relationships and knowledge in large volumes of data, combining methods from statistics, machine learning and database systems. The name itself is somewhat misleading: it is not the data that are extracted, but useful knowledge discovered from them.
A piece of the KDD process
Data mining is usually placed as the analysis step within a broader process, knowledge discovery in databases (KDD). Usama Fayyad, Gregory Piatetsky-Shapiro and Padhraic Smyth defined it in 1996 as “the non-trivial process of identifying valid, novel, potentially useful and understandable patterns in data.” In that scheme, mining is the moment when the algorithms are applied; before it come selection and cleaning, and after it, interpretation. Primary source.
Its tasks
Data mining groups together several tasks. Classification assigns each case to a category; regression predicts a numerical value; clustering discovers natural groups without prior labels; association rules find what things go together—the classic example is “market basket analysis”—; and anomaly detection locates cases that deviate from the norm, useful for spotting fraud or failures. Primary source.
How a project is organized: CRISP-DM
The reference methodology for structuring a project is CRISP-DM (Cross-Industry Standard Process for Data Mining). It divides the work into six phases—business understanding, data understanding, preparation, modeling, evaluation and deployment—and is cyclical: it is not traveled in a straight line, but by returning to earlier steps. Primary source.
Neighbors and limits
It is worth not confusing terms that overlap. Data mining focuses on discovering patterns; machine learning, on predicting; data science is a broader field that encompasses both; and big data refers to the size of the data, not a technique. Finally, because it works with often personal data, data mining raises serious privacy and ethics demands, with legal frameworks such as the European GDPR that regulate its use. Primary source.
Pieces using this term
- AI Act starts requiring transparency from major AI models (2025-08-02)
- Lawsuits Put Generative AI Training to the Test (2023-01-27)
This article was produced with artificial intelligence under human editorial oversight.