Types of Data


  • Data set:- A data set can often be viewed as collection of data objects, other names for data objects are record, point, vector, pattern etc.

  • Attribute:- An attribute is a property or characterstic of an object that may vary, either from one object to another or from one time to another.

  • Measurement scale:- A measurement scale is a rule (function) that associates a numerical or symbolic value with an attribute of an object.

Types of an attribute

An attribute can be classified on various basis, some of them are as follows:-

  1. On the basis of properties of numbers

  2. On the basis of number of values

Attribute classification on the basis of properties of numbers

It is a useful and simple way to classify the type of an attribute, in this an attribute is classified on the basis of underlying properties of numbers of value that it can hold.

The following properties of numbers are typically used to describe attributes:-

  1. Distinctness = or !=
  2. Order <, <=, >, >=
  3. Addition + and -
  4. Multiplication * and /

Using these properties we can classify attributes into 4 categories.

  1. Nominal
  2. Ordinal
  3. Interval
  4. Ratio
NominalThe values of a nominal attribute are just different names; i,e nominal values on provide enough information to distinguish one object from another (=, !=)zip codes, employee ID, eye color, gender
OrdinalThe values of an ordinal attributes provide enough information to order objects (<, >)hardess of metal, (good, better, best), grades, street numbers
IntervalFor interval attributes difference between values are meaningful, i.e, a unit of measurement exists (+, -)calender dates, temperature
Ratiofor ratio variables both difference and ratios are meaningful (*,/)counts, age, mass, length, electrical current

Attribute classification on the basis of number of values

It is a way of distinguishing between attributes is by the number of values they can take

  1. Discrete:- A discrete attribute has a finite or countably infinite set of values. Such attributes can be categorical, such as zip codes or ID numbers, or numeric such as counts. Binary attributes are special case of discrete attributes and assume only two values 0 and 1.

  2. Continues:- A continues attribute is an attribute whose values are real numbers. Example includes attribute such as temperature, height or weight. These are typically represented as floating point variables.

Asymmetric attributes

For asymmetric attributes, only presense (i.e., a non-zero attribute value) is regarded as important.


  1. Binary asymmetric attributes:- binary attributes where only non-zero values are important are called asymmetric binary attributes.

  2. Discrete asymmetric attributes:- if the value of a assymetric attribute is discrete then it is called discrete assymetric attribute.

  3. Continues asymmetric attributes:- if the value of a assymetric attribute is continues then it is called continues assymetric attribute.