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Summary

  • Data quality: accuracy, completeness, consistency, timeliness, believability, interpretability
  • Data cleaning: e.g. missing/noisy values, outliers
  • Data integration from multiple sources:
    • Entity identification problem
    • Remove redundancies
    • Detect inconsistencies
  • Data reduction
    • Dimensionality reduction
    • Numerosity reduction
    • Data compression
  • Data transformation and data discretization
    • Normalization
    • Concept hierarchy generation

Speaker notes:

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