Data Reduction, Data Discretization & Preprocessing Steps

Data Reduction

Obtains a reduced representation of a dataset that is much smaller in volume, yet closely maintains the integrity/analytical value of the original data — so mining on the reduced data is far more efficient and produces (nearly) the same results.

Techniques:

• Dimensionality reduction — reduces the number of attributes (PCA, attribute subset selection/wrapper methods).
• Numerosity reduction — replaces data with a smaller representation: parametric (regression models) or non-parametric (histograms, clustering, sampling, data cube aggregation).
• Data compression — encoding transformations (wavelet transforms, lossy/lossless compression) to shrink storage size.

Original Data (large) ──[reduction techniques]──▶ Reduced Data (small, representative)

Data Discretization

Converts continuous attribute values into a small number of interval labels or categories, reducing the number of distinct values and making the data suitable for algorithms that work with categorical data.

Methods:

• Binning — equal-width or equal-frequency bins.
• Histogram analysis — partitioning based on data distribution.
• Entropy-based discretization — splits chosen to maximize class purity (supervised).
• Concept hierarchy generation — organizes discretized values into multi-level hierarchies (e.g., Age → {Young, Middle-aged, Senior} → further grouped).

Continuous: 18, 22, 25, 40, 45, 60, 65
Discretized: [18-30)="Young", [30-55)="Middle-aged", [55-70)="Senior"

Major Steps in Data Preprocessing

Raw Data
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1. Data Cleaning  ────▶ handle missing values, noise, outliers, inconsistencies
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2. Data Integration ──▶ merge multiple heterogeneous sources into one dataset
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3. Data Reduction ────▶ shrink volume via dimensionality/numerosity reduction
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4. Data Transformation/Discretization ──▶ normalize, aggregate, bucket into
   │                                        mining-ready form
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Clean, Integrated, Reduced, Transformed Data → ready for Mining

1. Data Cleaning — fills missing values, smooths noisy data, identifies/removes outliers, resolves inconsistencies.
2. Data Integration — combines data from multiple databases/files/sources, resolving entity and schema conflicts.
3. Data Reduction — reduces data volume while preserving analytical results (as described above).
4. Data Transformation (including Discretization) — normalizes, aggregates, and converts continuous attributes into discrete categories for algorithms that need them.

These four steps together turn messy, real-world raw data into a clean dataset ready for accurate and efficient mining.