Data Preprocessing Questions
The common techniques used for data reduction are:
1. Attribute selection: This technique involves selecting a subset of relevant attributes from the original dataset. It helps in reducing the dimensionality of the data and removing redundant or irrelevant features.
2. Data cube aggregation: It involves aggregating data at different levels of granularity to reduce the size of the dataset. This technique is commonly used in data warehousing and OLAP (Online Analytical Processing) systems.
3. Sampling: Sampling involves selecting a representative subset of the data for analysis. It helps in reducing the computational complexity and processing time by working with a smaller sample instead of the entire dataset.
4. Discretization: Discretization involves transforming continuous variables into discrete intervals or categories. It helps in reducing the complexity of the data and simplifying the analysis.
5. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms the original variables into a new set of uncorrelated variables called principal components. It helps in capturing the most important information from the data while reducing its dimensionality.
6. Feature extraction: Feature extraction involves transforming the original features into a new set of features that are more informative and representative of the data. Techniques like linear discriminant analysis (LDA) and independent component analysis (ICA) are commonly used for feature extraction.
These techniques help in reducing the size, complexity, and dimensionality of the data, making it more manageable and suitable for analysis.