Basic approaches for Data generalization (DWDM)
Last Updated :
12 Oct, 2020
Data Generalization is the process of summarizing data by replacing relatively low level values with higher level concepts. It is a form of descriptive data mining.
There are two basic approaches of data generalization :
1. Data cube approach :
- It is also known as OLAP approach.
- It is an efficient approach as it is helpful to make the past selling graph.
- In this approach, computation and results are stored in the Data cube.
- It uses Roll-up and Drill-down operations on a data cube.
- These operations typically involve aggregate functions, such as count(), sum(), average(), and max().
- These materialized views can then be used for decision support, knowledge discovery, and many other applications.
2. Attribute oriented induction :
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