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Data Reduction 1: Dimensionality Reduction

• Curse of dimensionality
• When dimensionality increases, data becomes increasingly sparse
• Density and distance between points, which is critical to clustering, outlier analysis, becomes less meaningful
• The possible combinations of subspaces will grow exponentially
• Dimensionality reduction
• Avoid the curse of dimensionality
• Help eliminate irrelevant features and reduce noise
• Reduce time and space required in data mining
• Allow easier visualization
• Dimensionality reduction techniques
• Wavelet transforms
• Principal Component Analysis
• Supervised and nonlinear techniques (e.g., feature selection)