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.
| Rollo | Name | Result |
| 1 | Ali | Pass |
| 2 | Akram | Fail |
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.
| Attribute | Value |
| Categorical data | Lecturer, Assistant Professor, Professor |
| States | New, Pending, Working, Complete, Finish |
| Colors | Black, 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
| Attribute | Value |
| HIV detected | Yes, No |
| Result | Pass, Fail |
The binary attribute is of two types;
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:
| Attribute | Value |
| Gender | Male, 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
| Attribute | Value |
| HIV detected | Yes, No |
| Result | Pass, 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
| Attribute | Value |
| Grade | A, B, C, D, F |
| BPS- Basic pay scale | 16, 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
| Attribute | Value |
| Profession | Teacher, Bussiness Man, Peon etc |
| Postal Code | 42200, 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
| Attribute | Value |
| Height | 5.4…, 6.5….. etc |
| Weight | 50.09…. etc |
Next Tutorials with Similar Topics
- Type of Data that can be mined – Click Here
- Attributes Types – Click Here
- Mean, Median, Mode – Click Here
- Estimated Mean, Median, Mode – Click Here
- Data Quartiles – Click Here
- Box Plot for Data – Click Here
-
Variance and standard deviation of data in data mining – Click Here Calculator – Click Here
- Data skewness – Click Here
- Correlation analysis of numerical data in Data Mining – Click Here
- Correlation analysis of Nominal data with Chi-Square Test in Data Mining – Click Here
- Data discretization and its techniques in data mining – Click Here