In data mining, dimensionality reduction reduces the number of random variables under consideration by obtaining a smaller set of variables.

In data mining, dimensionality reduction reduces the number of random variables under consideration by obtaining a smaller set of variables.

• A. Social Engineering
• B. Footprinting
• C. Dimensionality Reduction
• D. Clustering

Answer: (C)

Dimensionality reduction is about cutting down the number of variables by deriving a smaller set of features from the data. In data mining, this helps handle the curse of dimensionality, speeds up algorithms, and can improve model performance by removing irrelevant or noisy information. You can achieve it by selecting a subset of existing features (feature selection) or by transforming the data into a lower-dimensional space (feature extraction), such as principal component analysis. The other terms aren’t about reducing the number of variables: social engineering is about manipulating people, footprinting is about gathering information on a target, and clustering is about grouping similar data points rather than shrinking the feature set.

Dimensionality reduction is about cutting down the number of variables by deriving a smaller set of features from the data. In data mining, this helps handle the curse of dimensionality, speeds up algorithms, and can improve model performance by removing irrelevant or noisy information. You can achieve it by selecting a subset of existing features (feature selection) or by transforming the data into a lower-dimensional space (feature extraction), such as principal component analysis. The other terms aren’t about reducing the number of variables: social engineering is about manipulating people, footprinting is about gathering information on a target, and clustering is about grouping similar data points rather than shrinking the feature set.