Which of the following methods is utilized to reduce the number of records in data reduction?

Which of the following methods is utilized to reduce the number of records in data reduction?

• A. Principal component analysis
• B. Stratified sampling
• C. Correlation analysis
• D. Decision tree induction

Answer: (B)

The method that effectively reduces the number of records in data reduction is stratified sampling. Stratified sampling involves dividing the population into distinct subgroups, or strata, based on specific characteristics. By subsequently drawing samples from each stratum, this technique ensures that the resulting sample is representative of the overall population and helps in reducing the dataset size while maintaining its key features. This method provides a systematic way to simplify the data without losing essential information. Principal component analysis focuses on transforming variables into fewer principal components but does not directly reduce the number of records. Correlation analysis examines relationships between variables but does not alter the number of records either. Decision tree induction is a method for classification and prediction that uses the entire dataset to build a decision tree, rather than reducing the record count. Thus, the practical approach to reducing the dataset in terms of number of records while still preserving representative information is through stratified sampling.

The method that effectively reduces the number of records in data reduction is stratified sampling. Stratified sampling involves dividing the population into distinct subgroups, or strata, based on specific characteristics. By subsequently drawing samples from each stratum, this technique ensures that the resulting sample is representative of the overall population and helps in reducing the dataset size while maintaining its key features. This method provides a systematic way to simplify the data without losing essential information.

Principal component analysis focuses on transforming variables into fewer principal components but does not directly reduce the number of records. Correlation analysis examines relationships between variables but does not alter the number of records either. Decision tree induction is a method for classification and prediction that uses the entire dataset to build a decision tree, rather than reducing the record count.

Thus, the practical approach to reducing the dataset in terms of number of records while still preserving representative information is through stratified sampling.