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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)

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