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Discrete vs continuous attributes
Short answer
Discrete attributes take finite/countable values (integers, categories), obtained by counting. Continuous attributes take real values in a range, obtained by measuring. Discrete examples: number of students, zip code; continuous: height, weight, temperature. Discrete uses bar charts; continuous uses histograms. Continuous can be discretized for some algorithms.
Differentiate discrete and continuous attributes with examples.
A discrete attribute takes a finite or countably infinite set of values, often integers. A continuous attribute takes real values within a range and can have any number of decimal places.
| Basis | Discrete | Continuous |
|---|---|---|
| Values | Finite or countable set | Infinite, any value in an interval |
| Obtained by | Counting | Measuring |
| Representation | Integers, categories, binary 0/1 | Floating-point numbers |
| Gap between values | Distinct gaps | No gaps; can be divided further |
| Examples | Number of students, zip code, set of words, yes/no, gender | Height, weight, temperature, time, distance |
| Typical graph | Bar chart | Histogram, line graph |
| Mining use | Used directly by counting methods (Apriori, Naive Bayes) | Often discretized for tree/association algorithms; distance measures work directly |
Notes
- Binary attributes (0/1, true/false) are a special case of discrete attributes.
- Continuous values are stored with limited precision in a computer, so in practice they are represented as floating-point numbers.
- A continuous attribute can be converted to discrete by discretization (e.g. marks 0-100 to grades A-F); a discrete one can be treated as continuous when it has many ordered values (e.g. salary in rupees).
✓ Answer checked by Claude Opus 5.5 (high effort)
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