Data mining is the process of analysing large datasets to discover useful patterns, relationships, trends, or predictions. Its main techniques include classification, clustering, and regression, which differ according to the type of pattern or output required.
Data mining is often applied to data stored in a data warehouse, where data from multiple sources has been integrated and prepared for analysis. A typical process involves cleaning the data, selecting relevant attributes, applying an algorithm, evaluating the results, and interpreting the discovered pattern.
| Technique | How it works | Example |
|---|---|---|
| Classification | Uses labelled training data to assign records to predefined categories. It is a supervised learning technique. | Classifying an email as “spam” or “not spam” based on known examples. |
| Clustering | Groups records according to similarities without using predefined labels. It is an unsupervised learning technique. | Dividing customers into groups based on purchasing behaviour. |
| Regression | Uses relationships between variables to predict a continuous numerical value. It is normally supervised because known input-output examples are used to construct the model. | Predicting a house price from its size, location, and age. |
For classification and regression, a model is trained using historical data containing known outcomes. It can then process new records: classification produces a category, while regression produces a numerical prediction.
Clustering does not begin with known outcomes. Instead, an algorithm measures similarities or distances between data points and forms groups whose members share characteristics. Analysts must then interpret what those clusters represent.
A common misconception is that data mining simply means collecting or storing data. Data storage preserves data, whereas data mining actively analyses it to extract information that may support decision-making.
In an IB Computer Science HL response on A3.4 Alternative databases and data warehouses, define data mining and distinguish the three techniques by their outputs: category, group, or numerical value. For an explain question, state how the technique works and provide a relevant example rather than merely naming it.
