attributes types in data mining

 

What is an Attribute?

The attribute can be defined as a field for storing the data that represents the characteristics of a data object. The attribute is the property of the object. The attribute represents different features of the object.  For example, hair color is the attribute of a lady. Similarly, rollno, and marks are attributes of a student. An attribute vector is commonly known as a set of attributes that are used to describe a given object.
Type of attributes
We need to differentiate between different types of attributes during Data-preprocessing. So firstly, we need to differentiate between qualitative and quantitative attributes.
1. Qualitative Attributes such as Nominal, Ordinal, and Binary Attributes.
2. Quantitative Attributes such as Discrete and Continuous Attributes.
There are different types of attributes. some of these attributes are mentioned below;

Example of attribute

In this example, RollNo, Name, and Result are attributes of the object named as a student.

RolloNameResult
1AliPass
2AkramFail

Types Of attributes

  • Binary
  • Nominal
  • Ordinal Attributes

  • Numeric
    • Interval-scaled
    • Ratio-scaled

Nominal Attributes

Nominal data is in alphabetical form and not in an integer. Nominal Attributes are Qualitative Attributes.

Examples of Nominal attributes

In this example, sates and colors are the attribute and New, Pending, Working, Complete, Finish and Black, Brown, White, and Red are the values.

AttributeValue
Categorical dataLecturer, Assistant Professor, Professor
StatesNew, Pending, Working, Complete, Finish
ColorsBlack, Brown, White, Red

Binary Attributes

Binary data have only two values/states. For example, here HIV detected can be only Yes or No.
Binary Attributes are Qualitative Attributes.

Examples of Binary Attributes

AttributeValue
HIV detectedYes, No
ResultPass, Fail

The binary attribute is of two types;

  1. Symmetric binary
  2. Asymmetric binary

Examples of Symmetric data

Both values are equally important. For example, if we have open admission to our university, then it does not matter, whether you are a male or a female. 

Example:

AttributeValue
GenderMale, Female

Examples of Asymmetric data

Both values are not equally important. For example, HIV detected is more important than HIV not detected. If a patient is with HIV and we ignore him, then it can lead to death but if a person is not HIV detected and we ignore it, then there is no special issue or risk.

Example

AttributeValue
HIV detectedYes, No
ResultPass, Fail

Ordinal Attributes

All Values have a meaningful order.  For example, Grade-A means highest marks, B means marks are less than A, C means marks are less than grades A and B, and so on. Ordinal Attributes are Quantitative Attributes.

Examples of Ordinal Attributes

AttributeValue
GradeA, B, C, D, F
BPS- Basic pay scale16, 17, 18

Discrete Attributes

Discrete data have a finite value. It can be in numerical form and can also be in a categorical form. Discrete Attributes are Quantitative Attributes.

Examples of Discrete Data

AttributeValue
ProfessionTeacher, Bussiness Man, Peon etc
Postal Code42200, 42300 etc

Example of Continuous Attribute

Continuous data technically have an infinite number of steps.

Continuous data is in float type. There can be many numbers in between 1 and 2. These attributes are Quantitative Attributes.

Example of Continuous Attribute

AttributeValue
Height5.4…, 6.5….. etc
Weight50.09….  etc

Watch Video Lecture

Next Tutorials with Similar Topics

  1. Type of Data that can be mined – Click Here
  2. Attributes Types – Click Here
  3. Mean, Median, Mode – Click Here
  4. Estimated Mean, Median, Mode – Click Here
  5. Data Quartiles – Click Here
  6. Box Plot for Data – Click Here
  7. Variance and standard deviation of data in data mining – Click Here   Calculator –  Click Here

  8. Data skewness – Click Here
  9. Correlation analysis of numerical data in Data Mining – Click Here
  10. Correlation analysis of Nominal data with Chi-Square Test in Data Mining – Click Here
  11. Data discretization and its techniques in data mining – Click Here

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Prof. Fazal Rehman Shamil
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