What are the data reduction techniques?
Table of Contents
What are the data reduction techniques?
Data Reduction in Data Mining
- Data Cube Aggregation: This technique is used to aggregate data in a simpler form.
- Dimension reduction:
- Data Compression:
- Numerosity Reduction:
- Discretization & Concept Hierarchy Operation:
Which techniques is used for numerosity reduction?
Methods for Numerosity reduction are:
- Regression or log-linear model (parametric).
- Histograms, clusturing, sampling (non-parametric).
Which techniques can be used as a data reduction technique because it allows a large dataset to be represented by a much smaller?
Sampling: Sampling can be used for data reduction because it allows a large data set to be represented by a much smaller random data sample (or subset).
Which one is not a data reduction technique?
Discussion Forum
| Que. | Which one is not a data reduction strategy |
|---|---|
| b. | Dimension reduction |
| c. | Data compression |
| d. | Data cube aggregation |
| Answer:Data Generalization |
Why do we need to apply data reduction techniques list data reduction techniques?
Data reduction techniques can be applied to obtain a reduces data should be more efficient yet produce the same analytical results. Discretization and concept hierarchy generation are powerful tools for data mining, in that they allow the mining of data at multiple levels of abstraction.
How sampling can be used as a data reduction technique with an example?
Sampling: Sampling can be used for data reduction because it allows a large data set to be represented by a much smaller random data sample (or subset). Data Cube Aggregation: Data cube aggregation involves moving the data from detailed level to a fewer number of dimensions.