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Table of contents
1.
Introduction
2.
What is KDD (Knowledge Discovery in Databases) in Data Mining?
3.
KDD Process
3.1.
Data Cleaning
3.2.
Data Integration
3.3.
Data Selection
3.4.
DataTransformation
3.5.
Data Mining
3.6.
Pattern Evaluation
3.7.
Knowledge Presentation
4.
Advantages of KDD in Data Mining
5.
Disadvantages of KDD in Data Mining
6.
Difference between KDD and Data Mining 
7.
Frequently Asked Questions
7.1.
Where is KDD used?
7.2.
What is the role of KDD?
7.3.
What are some issues in the KDD process?
7.4.
What is KDD in Data Mining?
7.5.
What is the data preparation stage of knowledge discovery process?
7.6.
What are the 4 steps of KDD?
8.
Conclusion
Last Updated: May 19, 2024
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KDD in Data Mining

Author Nidhi Kumari
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Master Python: Predicting weather forecasts
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Ashwin Goyal
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Introduction

Data mining is the process of extracting trends, patterns, insights, or data from huge amounts of data, sometimes known as big data. KDD stands for Knowledge Discovery in Databases. Although KDD and data mining are closely linked concepts, they are not identical. Let’s discuss the KDD process in detail, along with the advantages and disadvantages of KDD in data mining.

KDD in Data Mining

What is KDD (Knowledge Discovery in Databases) in Data Mining?

KDD is a method of extracting relevant, unknown, and useful data from massive databases(also known as big data). It is useful for researchers in various fields, such as machine learningartificial intelligence, pattern recognition, data visualisation etc. 

Knowledge Discovery in Databases (KDD) refers to the process of extracting useful insights, patterns, and knowledge from large volumes of data. It is a crucial component of the broader field of data mining, which encompasses various techniques and methodologies for analyzing and interpreting data to uncover hidden patterns, trends, and relationships.

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KDD Process

KDD is an iterative method and extracts valuable data after numerous repetitions of the processes.KDD involves several steps, each advancing the goal of extracting useful information from data. These steps are as follows:

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

Let’s discuss these steps in detail.

Data Cleaning

Data cleaning ensures the data is high quality and appropriate for analysis. it is the process of locating and fixing errors in a dataset. Data cleaning is crucial to fix missing and noisy values of real-world data that can negatively affect the system’s accuracy. It helps in improving overall data quality.

Data Integration

The process of merging data from various sources into a single, complete view is known as data integration.  It is a complicated and repetitive process. Integration involves steps like ETL mapping, and conversion and begins after cleaning.

Real-world data is frequently distributed over numerous databases, servers and files, making it challenging to analyse and derive valid data without integrating them. Data integration offers the framework for efficient analysis and knowledge discovery throughout the KDD process. It helps data scientists draw in-depth results from various distributed data sources. 

Data Selection

One of the primary steps in the KDD process is data selection. It is described as selecting the proper data source, kind, and instruments to gather the data.  It prepares the foundation for the KDD process's further data transformation, mining, and knowledge presentation processes.

DataTransformation

Data transformation entails converting and altering the data to make the original data acceptable for analysis and knowledge discovery. It transforms raw data into a form that may be used for modelling and analysis. 

Often very project-specific, this step can be crucial for the overall KDD project's success. The changes are made to solve various issues and prepare the data for other steps like mining and pattern recognition.

Data Mining

To find patterns and associations that can be used to solve problems through data analysis, vast data sets are sorted using a process called data mining. Gaining insightful knowledge and using it to create well-informed decisions are the ultimate goals of data mining. 

Enterprises can forecast future trends and make better business decisions by using data mining techniques and technologies. Data mining is commonly used in several fields for consumer segmentation, fraud detection, market analysis in business and marketing, and disease evaluation and diagnosis in healthcare.

Pattern Evaluation

Pattern evaluation is the process of finding strictly increasing patterns that indicate knowledge based on specific metrics. Not every pattern exists equally; some patterns might be useless, and others might be highly valuable and informative. 

Methods of pattern evaluation play a part in such kind of situation. Pattern evaluation is an essential step in the KDD process, especially when looking for connections and patterns in huge datasets.

Knowledge Presentation

It is the final step of the KDD process. When knowledge is presented to a user visually through tables, graphs, charts, trees, matrices, etc., it is known as knowledge representation. 

It is used to facilitate well-informed decision-making and problem-solving. The main objective of knowledge presentation is to explain the insights and conclusions produced through data mining clearly.

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Advantages of KDD in Data Mining

Some of the advantages of KDD are as follows:

  • KDD helps in data-driven decision-making.
     
  • It is also used for pattern recognition and fraud detection systems.
     
  • It improves the performance of firms and organisations.
     
  • One of the critical features of KDD is to uncover hidden patterns in the datasets.
     
  • It helps in the detection of anomalies in databases.
     
  • It plays a vital role in research and discovery fields.
     
  • It provides feedback after analysing datasets helping companies to modify their process.

Disadvantages of KDD in Data Mining

Some of the disadvantages of KDD are as follows:

  • KDD is a complex process.
     
  • It heavily depends on the quality of the data. So, data quality maintenance is required for the KDD process.
     
  • Analysing large amounts of data can raise security and privacy issues.
     
  • Overfitting data in the KDD process can decrease the system's performance.
     
  • To select algorithms and analyse patterns, great human expertise is required.

Difference between KDD and Data Mining 

FeatureKnowledge Discovery in Databases (KDD)Data Mining
DefinitionProcess of extracting useful patterns from data.Subset of KDD, specifically focused on pattern extraction.
ScopeBroader, encompasses the entire process of knowledge discovery.Specific, concentrating on the algorithmic extraction of patterns.
StagesInvolves stages like data selection, preprocessing, transformation, data mining, interpretation, and evaluation.Primarily associated with the data mining phase within KDD.
GoalAims at uncovering patterns, trends, and knowledge from data.Focuses on applying algorithms to identify patterns within data.
ComponentsEncompasses data cleaning, integration, selection, transformation, data mining, pattern evaluation, and knowledge representation.Primarily involves applying algorithms for pattern discovery.
InterdisciplinaryMultidisciplinary approach, involving database management, statistics, machine learning, and domain knowledge.Often seen as a subfield of machine learning and statistics, with a narrower focus.
ApplicationUsed in various domains for decision support and strategic planning.Applied in diverse fields for predictive modeling, classification, clustering, and anomaly detection.

Frequently Asked Questions

Where is KDD used?

KDD is used across diverse domains like business, healthcare, finance, and science to extract valuable patterns and insights from large volumes of data.

What is the role of KDD?

KDD plays a vital role in uncovering hidden patterns, trends, and knowledge from data, providing valuable insights for decision-making and strategic planning in various fields.

What are some issues in the KDD process?

Since KDD requires gathering and analysing vast volumes of data, privacy issues may arise. KDD can be a challenging procedure that calls for specialised training and understanding to implement and fully grasp the outcomes.

What is KDD in Data Mining?

KDD (Knowledge Discovery in Databases) in data mining refers to the process of extracting valuable insights, patterns, and knowledge from large datasets. It involves various stages such as data selection, preprocessing, mining, pattern evaluation, and knowledge presentation.

What is the data preparation stage of knowledge discovery process?

The data preparation stage involves cleaning, transforming, and organizing selected data for analysis. Tasks include removing noise, handling missing values, and converting data into a suitable format. This stage ensures that the data is ready for analysis in subsequent stages of the knowledge discovery process.

What are the 4 steps of KDD?

The four steps of KDD (Knowledge Discovery in Databases) include data selection, preprocessing, data mining, and pattern evaluation. These steps collectively involve identifying relevant data sources, preparing the data for analysis, applying data mining techniques to uncover patterns and trends, and evaluating the discovered patterns for significance and usefulness.

Conclusion

In this article, we extensively discussed KDD in Data Mining. KDD is a method of extracting relevant, unknown, and useful data from massive databases. It is an iterative method and extracts valuable data after numerous repetitions of the processes.KDD involves several steps, each advancing the goal of extracting useful information from data.

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