Data Preprocessing

Data Preprocessing Core Framework

Data preprocessing is a foundational phase in data science that transforms raw, real-world data into a clean, integrated, and optimized format suitable for downstream mining algorithms.

3.2

Data Cleaning

Resolves data quality flaws by explicitly handling missing values and smoothing out noisy data structures to minimize system bias.

3.3

Data Integration

Consolidates multi-source schemas, eliminates entity redundancies, tracks value conflicts, and clears duplicate metadata profiles.

3.4

Data Reduction

Compresses volume and dimension footprints via mechanisms like Wavelets, PCA, Sampling, Histograms, and Data Cube Aggregation.

3.5

Transformation & Discretization

Standardizes ranges through data normalization, structural binning, and histogram cluster segmentations into actionable categorical intervals.

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