Answer: (iv) Reduce data volume but produce the same analysis results

Data Reduction techniques shrink the size/volume of a dataset while preserving its analytical integrity, so that mining on the reduced data yields (approximately) the same results as mining on the full data — but faster and cheaper.

Common data reduction techniques:

• Dimensionality reduction (PCA, attribute subset selection)
• Numerosity reduction (sampling, histograms, clustering, regression models)
• Data compression (wavelet transforms, lossy/lossless encoding)
• Data cube aggregation

Note: this is distinct from (i) "delete irrelevant data," which is closer to feature/attribute selection — a technique used within data reduction, but the reduction's actual goal (per the standard definition) is volume reduction with result-preserving fidelity.