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Table of contents
1.
Introduction
2.
What is KDD?
2.1.
Applications of KDD
3.
What is Data Mining?
3.1.
Applications of Data Mining
4.
Data Mining vs KDD
5.
Frequently Asked Questions
5.1.
What are some of the difficulties with KDD?
5.2.
What are some advantages of using the KDD method?
5.3.
What are some of the difficulties with data mining?
5.4.
What are some advantages of using data mining?
6.
Conclusion
Last Updated: Mar 27, 2024

Data Mining vs KDD

Author Sinki Kumari
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Introduction

The process of identifying patterns, trends, variations, and correlations in big data is known as data mining. Data mining is one stage of KDD, a more complex process. It is a multi-step process that is iterative and includes various stages.

Data Mining vs KDD

Although data mining and KDD are crucial elements in data analysis and knowledge extraction, they have different functions and objectives. In this article, we will explore the concepts of data mining and KDD, understand their tasks, and highlight their differences.

What is KDD?

KDD stands for knowledge discovery in databases. The KDD method is a complex and iterative approach to knowledge extraction from big data. Extraction of knowledge from massive data is accomplished through the intricate and iterative KDD process. 

It is an extensive method that includes data mining as one of its steps. It includes utilising a variety of algorithms and statistical methods to sort through large amounts of data and identify relevant and valuable data. The following steps make up the cyclical process of KDD:

  • Data cleaning
     
  • Data Integration
     
  • Data selection
     
  • Data transformation
     
  • Data mining
     
  • Pattern evaluation
     
  • Knowledge presentation

Applications of KDD

Some of the crucial applications of KDD are as follows:

  • Business and Marketing: User analysis, market prediction, Segmenting clients, and focused marketing are all examples of business and marketing databases.
     
  • Manufacturing: Predictive system analysis, process improvement, and quality control.
     
  • Finance: Fraud investigation, evaluation of credit risk, and stock market research in the finance sector can be analysed using the KDD method.
     
  • Healthcare: Drug progress, patient monitoring, and disease diagnosis from a large set of patient data.
     
  • Scientific research: Identifying patterns in massive scientific databases, such as genetics, astronomy, and climate.
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What is Data Mining?

Data mining is identifying patterns and extracting details about big data sets using intelligent methods, including statistics, machine learning, and database systems. In the KDD method, the fifth phase is called "data mining." It is the analytical stage of the (KDD). Various algorithms to extract patterns from big data are generally called the "core step" in this process.

Applications of Data Mining

Some of the crucial applications of data mining are as follows:

  • Risk evaluation: Data mining can be used to evaluate the probability of an occurrence, such as the defects in a product.
     
  • Recommendation: Data mining can make product suggestions to customers based on their history or browsing patterns.
     
  • Fraud detection: Data mining can spot patterns of shady behaviour, such as fraudulently using a credit card or inflating a policy.
     
  • Medical diagnosis: Data mining can be utilised to diagnose medical disorders by seeing trends in medical records.

Data Mining vs KDD

Now we will discuss the main difference covering data mining vs KDD.

Key Features Data Mining KDD
Basic Definition Data mining is the process of identifying patterns and extracting details about big data sets using intelligent methods. The KDD method is a complex and iterative approach to knowledge extraction from big data.
Goal To extract patterns from datasets. To discover knowledge from datasets.
Scope In the KDD method, the fourth phase is called "data mining." KDD is a broad method that includes data mining as one of its steps.
Used Techniques
  • Classification

     
  • Clustering

     
  • Decision Trees

     
  • Dimensionality Reduction

     
  • Neural Networks

     
  • Regression

     
  • Data cleaning

     
  • Data Integration

     
  • Data selection

     
  • Data transformation

     
  • Data mining

     
  • Pattern evaluation

     
  • Knowledge Presentation
Example Clustering groups of data elements based on how similar they are. Data analysis to find patterns and links.

 

Frequently Asked Questions

What are some of the difficulties with KDD?

You might not be able to find the patterns you're looking for using the KDD algorithms when your data is inaccurate or lacking. The KDD technique could produce wrong results when the data is incomplete.

What are some advantages of using the KDD method?

It may enable you to utilise your data to make better choices. It can aid in the discovery of hidden trends and patterns in your data. You can use it to find new possibilities. You might use it to enhance your goods and services.

What are some of the difficulties with data mining?

It might be difficult to select the best data mining technique for a given task because there are so many different algorithms accessible. It is crucial to take action to reduce bias in data mining algorithms because these algorithms can be biased.

What are some advantages of using data mining?

By spotting possible issues before they occur, data mining can help companies lower risk. Businesses may find new prospects with its assistance. Businesses can use it to find opportunities to increase their efficiency.

Conclusion

In this article, we extensively discussed data mining vs KDD. We also covered the applications of data mining and KDD.

We hope this article helps you. To read more about data mining, you can visit more articles.

 

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