- Year not given
Data discretization
Short answer
Discretization divides numeric values into intervals (e.g., equal-width, equal-frequency binning) or uses supervised methods like entropy-based splitting. It reduces data size and supports algorithms requiring categorical input, at the cost of some information loss.
Describe briefly about Data Discretization and how it can be achieved.
Data discretization transforms a continuous (numeric) attribute into a finite number of intervals or categorical labels, and the interval labels replace the actual values. It reduces data size, makes data easier to understand, and is needed by algorithms that accept only categorical input (e.g. Apriori, ID3, Naive Bayes). Example: age 0-18 → "youth", 19-59 → "adult", 60+ → "senior". A concept hierarchy is built when the labels are generalised further.
Techniques
Unsupervised (ignore class label)
- Equal-width binning: divide the range into k intervals of equal width w = (max − min)/k. Simple but sensitive to outliers.
- Equal-frequency (equal-depth) binning: each bin has roughly the same number of values. Good for skewed data.
- Clustering: group values by k-means and use cluster boundaries.
Supervised (use class label)
- Entropy-based (decision-tree) discretization: choose the split point that gives the highest information gain, then split recursively.
- ChiMerge: bottom-up, merge adjacent intervals that have similar class distribution using χ² test.
Other
- Histogram analysis and concept hierarchy generation (natural hierarchy such as day → month → year).
Example (equal-depth): prices 4, 8, 15, 21, 21, 24, 25, 28, 34 → Bin 1 (4, 8, 15), Bin 2 (21, 21, 24), Bin 3 (25, 28, 34).
Discretization loses some information, so the number of intervals should be chosen carefully.
✓ Answer checked by Claude Opus 5.5 (high effort)