Classification in Data Mining – A Beginner’s Guide

# Classification in Data Mining – A Beginner’s Guide

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Rashmi Karan
Manager - Content
Updated on Dec 11, 2023 14:39 IST

Classification in data mining is a crucial technique that contributes 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 acknowledgements and data instances. This blog covers the essentials of data mining system classification, the common usage of classification of data mining systems, classification requirements, etc.

Classification is a crucial technique in data mining that is attributed to data classification 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 classifying a data mining system is to determine the input variables. Classification is also dependent on a series of acknowledgements and data instances. This blog covers the essentials of classification in data mining, the steps involved in classification, and requirements, among other topics.

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## 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 based on 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

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## Classification of Data Mining Systems

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

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.

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### 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

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### Classification According to the Information/Pattern Obtained

Pattern identification is a crucial outcome of data mining. For that, algorithms must 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 the Types of Databases Extracted

Data mining involves the analysis of several 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
1. 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.

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## 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 more accurate decisions. 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.

## FAQs - Classification of Data Mining Systems

How can data mining systems be classified based on functionality?

Data mining systems can be classified based on functionality into the following categories:

• Descriptive Data Mining: Focuses on uncovering patterns, trends, and insights within data to understand the information better.
• Predictive Data Mining: Concentrates on making predictions or classifications based on historical data, using algorithms to forecast future outcomes.

How are data mining systems classified based on the kind of data they handle?

Depending on the data type they analyse, data mining systems can be categorised as text mining, web mining, spatial data mining, and multimedia mining systems.

What is text mining in data mining systems?

Text mining focuses on extracting valuable information and knowledge from unstructured textual data, such as documents, emails, and social media content.

What are spatial data mining systems used for?

Spatial data mining systems analyse geographic or spatial data to discover patterns and relationships, which can be valuable for applications like location-based services and urban planning.

How can data mining systems be classified based on their processing capabilities?

Data mining systems can be classified into batch processing and real-time processing systems. Batch processing systems analyse data in large batches, while real-time processing systems provide immediate insights as data streams in.