Classification in Data Mining – A Beginner’s Guide

Classification in data mining is a crucial technique that attributes to the classification of data. In the classification process, you need to make decisions to bring the data together and define the criteria to classify the data sets. The first step towards classification is to determine the input variables. Classification is also dependent on a series of acknowledgments and data instances.

This blog covers the essentials of data mining system classification, the common usage of classification of data mining systems, classification requirements, among other topics.

To learn more about data mining, read – What is Data Mining?

Classification is a crucial technique in data mining that attributes to the classification of data and is used to predict group membership for data instances. In the classification process, you need to make decisions to bring the data together and define the criteria to classify the data sets. The first step towards the classification of a data mining system is to determine the input variables. Classification is also dependent on a series of acknowledgments and data instances. This blog covers the essentials of classification in data mining, steps involved in classification, requirements, among other topics.

You May Like – Key Data Mining Applications, Concepts, and Components

Introduction

• Classification trains the data set and constructs the classification model
• Classification forecasts the value of the class label

For example – Students earn their class basis their marks obtained in the university/institute/college

If x >= 65, then First class with distinction

If 60<= x<= 65, then First class

If 55<= x<=60, then Second class

If 50<= x<= 55, then Pass class

Must Explore – Data Mining Courses

Classification of Data Mining Systems

Classification of data mining System is based on several criteria, which include –

Classification According To Adapted Application

Classification according to the adapted application refers to one or more data mining systems adapted in specific areas like Telecommunications, Finance, Stock Markets, E-mails, Medicine, Sports, etc. A generic data mining system is not a perfect fit for domain-specific mining tasks and may require application-specific methods.

Read our blog – What is data science?

Classification According To the Type of Technique Used

Classification according to the type of underlying data mining techniques is explained as per the degree of user interaction. A sophisticated data mining system adopts multiple techniques for better results. Such approaches include –

• Machine Learning
• Data Visualization
• Data Warehousing
• Pattern Recognition
• Statistical Methods
• Neural Networks
• Database-Oriented Techniques
• Data Storage

Interesting Read – Top Data Mining Algorithms You Should Learn

Classification According to the Information/Pattern Obtained

Pattern identification is a crucial outcome of data mining and for that, algorithms need to go through the different types of data sets and provide the most relevant and accurate outcomes. This type of data mining system classification is based on functionalities such as characterization, association, discrimination, correlation, prediction, etc.  

Classification According to Types of Databases Extracted

Data mining involves the analysis of a number of databases, where every database handles a defied data model containing different data types. Classification according to the types of databases extracted helps to segregate such databases, particularly based on the type of data or model used.

Classification Requirements

The two important steps of classification are:

1. Model construction

• Assigning a predefined class label to every sample tuple or object, also called training data sets
• Using the constructed model as classification rules, decision trees, or mathematical formulae

2. Model usage

• Classification of unknown tuples or objects using the constructed model
• Comparison of a class label of the test sample with the resultant class label
• Comparison of the accuracy of the model

Note – Test sample data and training data samples are always different.

Must Read: Top 10 Machine Learning Algorithms for Beginners

Conclusion

Data Mining has applications across all domains now and renowned companies like Google, Netflix, Facebook, etc. use different techniques to extract data that help them make decisions that are more accurate. These general concepts about classification will help you get an idea of the subject. We will cover more in-depth topics related to classification in data mining in the future.

---

If you have recently completed a professional course/certification, click here to submit a review.