Question

What is importance of data reduction in data mining?

Answer 1

Answer:

Explanation:

Data Reduction In Data Mining

A database or date warehouse may store terabytes of data.So it may take very long to perform data analysis and mining on such huge amounts of data.

Data reduction techniques can be applied to obtain a reduced representation of the data set that is much smaller in volume but still contain critical information.

Data Reduction Strategies:-

1.Data Cube Aggregation

Aggregation operations are applied to the data in the construction of a data cube.

2.Dimensionality Reduction

In dimensionality reduction redundant attributes are detected and removed which reduce the data set size.

3.Data Compression

Encoding mechanisms are used to reduce the data set size.

4.Numerosity Reduction

In numerosity reduction where the data are replaced or estimated by alternative.

5.Discretisation and concept hierarchy generation

Where raw data values for attributes are replaced by ranges or higher conceptual levels.

Answer 2

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Data reduction is a process that reduced the volume of original data and represents it in a much smaller volume. Data reduction techniques ensure the integrity of data while reducing the data. The time required for data reduction should not overshadow the time saved by the data mining on the reduced data set.