---
title: "Discrete vs continuous attributes"
subject: "Data Mining"
chapter: 2
key: "semester_7/data_mining:discrete-vs-continuous"
source: https://www.bctengineer.com/q/semester_7/data_mining/discrete-vs-continuous
site: BCT Engineering
---

# Discrete vs continuous attributes

Data Mining · Chapter 2: Data Pre-processing

- Asked in: year not given
- Video: [Discrete v/s Continuous Data - What ? How ? || Discrete Data || Continuous Data || Basic Statistics](https://www.youtube.com/watch?v=Cg0W6mod9Hw) — Learning Champion
- Answer checked by Claude Opus 5.5 (high effort) on 2026-10-14
- Page: https://www.bctengineer.com/q/semester_7/data_mining/discrete-vs-continuous
- Whole chapter: https://www.bctengineer.com/md/semester_7/data_mining/chapter-2

## Question

Differentiate discrete and continuous attributes with examples.

## 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.

## Answer

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).

## Check yourself

The answer's key points as multiple-choice questions, with the correct option and why.

1. Which describes a discrete attribute?
   A. Finite or countably infinite set of values, obtained by counting
   B. Any real value in an interval, obtained by measuring
   C. Values with no gaps
   D. Always floating-point
   Answer: A. Discrete attributes have distinct values and are counted. (Key point: Discrete definition)

2. Which pair is correct?
   A. Discrete: height; continuous: zip code
   B. Discrete: number of students; continuous: height
   C. Discrete: temperature; continuous: gender
   D. Discrete: weight; continuous: yes/no
   Answer: B. Counts and categories are discrete; measured quantities like height are continuous. (Key point: Examples)

3. How can a continuous attribute be made discrete?
   A. By multiplying by 10
   B. By adding noise
   C. Discretization, e.g. marks 0-100 to grades A-F
   D. By PCA
   Answer: C. Discretization replaces ranges of continuous values by labels. (Key point: Converting)

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