Classification Techniques in Data Mining

Classification Techniques in Data Mining, Classification is the process of grouping things or items into groups or classes based on their affinities and resemblances in order to communicate the units of traits that may exist among a diverse group of people.

Classification Techniques in Data Mining

The different modes of classifications are,

1. Geographical

In this case, classification is according to place, area, or region.

2. Chronological

It is according to the lapse of time example monthly, yearly, etc…

3. Qualitative

Data are classified according to the attributes of the subjects or items. Example sex, qualification, color, etc…

4. Quantitative

Data are classified according to the magnitude of the numerical values example age, income, height, weight, etc…

What’s Neural Network? »

What are the objectives?

Broadly there are six objectives of classification,

1. To present the facts in a simple manner.

2. To draw attention to goods that have or do not have certain characteristics or features.

3. To aid in the comparison of different things.

4. To determine if various measures and their effects have a mutual relationship.

5. To show data in a way that is suitable for further processing.

6. To serve as a foundation for tabulation.

What do you understand by qualitative classification?

It is the classification on the basis of certain attributes or some qualities of items that cannot be measured quantitatively.

What do you understand by multifactor classification?

Multifactor classification refers to categorization criteria that are based on two or more factors.

The data is first classified into two or more classes based on one factor in this sort of classification.

Further classification is done on the basis of the second factor, and so on, for each component.

How to Change Legend Position in ggplot2 »

What are the different characteristics?

The different characteristics of classification are,

1. Exhaustive

The classes should be designed to cover all of the items in the set. They must also be complete and free of overlap. For example, the classes for material status should be married, single, widow, widower, divorcee, and deserted.

2. Stability

The classification should be consistent or standardized so that the results may be compared across studies or occasions.

3. Flexibility

The classification should be amenable according to different situations or requirements of the study.

4. Homogeneity

All classes’ measuring units should be the same, and like units should only be accommodated in one class.

5. Suitability

The classification should be done solely on the basis of the study’s goal.

For example, if you want to analyze people’s financial situations, it’s pointless to divide them into groups based on their skin color, hair color, and so on…

6. Arithmetic Accuracy

The total number of units should equal the sum of the number of units in all classes.

In the event of observations, the total of all observations should equal the total of all observations.

Grouping Data in R- Tidyverse Approach »

Subscribe to our newsletter!

[newsletter_form type=”minimal”]

You may also like...

Leave a Reply

Your email address will not be published. Required fields are marked *

eight − four =