Data Generalization

It converts low-level data attributes to high-level data attributes using concept hierarchy. This conversion from a lower level to a higher conceptual level is useful to get a clearer picture of the data. Data generalization can be divided into two approaches:

• Data cube process (OLAP) approach.
• Attribute-oriented induction (AOI) approach.

For example, age data can be in the form of (20, 30) in a dataset. It is transformed into a higher conceptual level into a categorical value (young, old).

Reference List

1. https://www.javatpoint.com/data-transformation-in-data-mining#:~:text=Data%20transformation%20is%20an%20essential,them%20into%20clean%2C%20usable%20data.