KDD Process in Data Mining: What You Need To Know?

As a working professional, you are familiar with terms like data, database, information, processing, etc. You must have also come across terms like data mining and data warehouse. We’ll talk about those two terms in detail later on, but there’s a far more elaborate methodology that encompasses the two terms mentioned above: KDD.

Before diving into the fundamentals of KDD, let’s get an insight into data mining.

What is Data Mining?

Data mining is the process of obtaining information from a vast dataset. It helps explore and identify user patterns and trends in datasets and uses algorithms to extract patterns from them. The interdisciplinary approach of data mining uses statistics, AI, ML, and database technology methods. 

Data mining has found its importance across many industries, such as finance, healthcare, education, etc. For instance, in the retail industry, data mining uses data-gathering techniques to examine the trends in purchasing and creates models to anticipate customers’ behavior. It is used to:

• Predict cancellations according to the clients’ past data. 
• Offer product or service recommendations based on past usage. 
• Using past transactional data spot and stop any fraudulent behavior. 
• Customers are grouped based on similar purchasing history; this helps in sending personalized marketing messages.  

Data mining and knowledge discovery are essential for businesses to identify patterns and make strategic decisions for the growth of the business. 

What is KDD?

KDD is referred to as Knowledge Discovery in Database and is defined as a method of finding, transforming, and refining meaningful data and patterns from a raw database in order to be utilised in different domains or applications.

The above statement is an overview or gist of KDD, but it’s a lengthy and complex process which involves many steps and iterations. Now before we delve into the nitty-gritty of KDD, let’s try and set the tone through an example.

Suppose, there’s a small river flowing nearby and you happen to be either one of a craft enthusiast, a stone collector or a random explorer. Now, you have prior knowledge that a river bed is full of stones, shells and other random objects. This premise is of the utmost importance without which one can’t reach the source.

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Next, depending on whom you happen to be, the needs and requirements may vary. This is the second most important thing to understand. So, you go ahead and collect stones, shells, coins or any artefacts that might be lying on the river bed. But that brings along dirt and other unwanted objects along as well, which you’ll need to get rid of in order to have the objects ready for further use.

At this stage, you might need to go back and collect more items as per your needs, and this process will repeat a few times or be completely skipped as per the conditions.

The collected objects need segregation into different types to better suit your application and are further required to be cut, polished or painted. This stage is called the transformation stage.

During this process, you gain an understanding of, for example, where you are more likely to find bigger stones of certain colouration – whether near the bank or deeper in the river, whether the artefacts are probable to be found upstream or downstream and so on. Data mining is an important part when you learn data science.

This helps in decoding patterns which can help in more efficient and quicker completion of tasks. What you eventually end up with is the discovery of knowledge that is refined, reliable and highly specific to your application.

Now, let’s dive into KDD in data mining in detail.

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What is KDD in Data Mining?

KDD in data mining is a programmed and analytical approach to model data from a database to extract useful and applicable ‘knowledge’. Data mining forms the backbone of KDD and hence is critical to the whole method.

It utilises several algorithms that are self-learning in nature to deduce useful patterns from the processed data. The process is a closed-loop constant feedback one where a lot of iterations occur between the various steps as per the demand of the algorithms and pattern interpretations.

What is the Use of the Knowledge Discovery Process in Data Mining?

The knowledge discovery process in data mining aims to identify hidden relationships, patterns, and trends in the sample data, which can be used for making predictions, recommendations, and decisions. KDD is an interdisciplinary and broad field used in many industries like healthcare, finance, e-commerce, marketing, etc. 

KDD in data mining is necessary for businesses and organizations since it enables them to get new knowledge and insights from the data. Knowledge discovery in databases can assist in improving customer experience, enhance the decision-making process, optimize operations, support strategic planning, and drive business growth. 

Essential Terms for Understanding KDD in Data Mining

If you seek an elaborate understanding of ‘what is KDD process?’, you must first get acquainted with certain terms. Some of these terms are listed below:

• Databases: Databases are organized collections of data stored on a computer. The information is structured since it is challenging to understand unstructured data, mainly from a large dataset. 
• Data mart: A data mart can be defined as a collection of interrelated databases. There can be different data sources, such as transactional data, operational data, client data, etc. The data passes through several stages. In the extraction stage, all the data is collected from various sources, followed by the transformational stage, where the data is sorted, processed, and then standardized. Finally, it is placed into the data mart. 
• Pattern: The valuable output from the analysis represents the dataset’s trends. KDD helps identify non-trivial patterns, which usually cannot be observed easily. 
• Knowledge: Interpreting and understanding the patterns helps decision-makers develop the necessary knowledge. The acquired knowledge must be novel and valuable. Ensuring that the knowledge is non-trivial, practical, and unique is crucial as it helps authorities make essential decisions about the business. 

Expectation from KDD

To explain the process of KDD, you must understand what the process is expected to achieve. These expectations include:

• Non-trivial: For example, you can find that people who purchase luxury items are wealthier than people who don’t. But, if you can discover the specific life events that might lead to purchasing luxury products, then the knowledge discovery is non-trivial. 
• Implicit knowledge: There are generally two sorts of knowledge: explicit and implicit. Explicit knowledge is usually apparent and evident. However, the knowledge embedded within the data is implicit. Implicit knowledge involves unwritten processes, trends, and rules that are challenging to discover.
• Previously unknown: KDD is expected to discover something new. The analysis will not be worth it if it only provides known information. The KDD process should provide information from the previously unknown dataset, allowing decision-makers to make a more informed decision. 
• Useful: Lastly, the knowledge should have practicality; otherwise, it will be useless. Hence, analysts have to sift through the data to find the pattern necessary for the business. 

Steps Involved in a Typical KDD Process

1. Goal-Setting and Application Understanding

This is the first step in the process and requires prior understanding and knowledge of the field to be applied in. This is where we decide how the transformed data and the patterns arrived at by data mining will be used to extract knowledge. This premise is extremely important which, if set wrong, can lead to false interpretations and negative impacts on the end-user.

2. Data Selection and Integration

After setting the goals and objectives, the data collected needs to be selected and segregated into meaningful sets based on availability, accessibility importance and quality. These parameters are critical for data mining because they make the base for it and will affect what kinds of data models are formed.

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3. Data Cleaning and Preprocessing

This step involves searching for missing data and removing noisy, redundant and low-quality data from the data set in order to improve the reliability of the data and its effectiveness. Certain algorithms are used for searching and eliminating unwanted data based on attributes specific to the application.

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4. Data Transformation

This step prepares the data to be fed to the data mining algorithms. Hence, the data needs to be in consolidated and aggregate forms. The data is consolidated on the basis of functions, attributes, features etc.

5. Data Mining

This is the root or backbone process of the whole KDD. This is where algorithms are used to extract meaningful patterns from the transformed data, which help in prediction models. It is an analytical tool which helps in discovering trends from a data set using techniques such as artificial intelligence, advanced numerical and statistical methods and specialised algorithms.

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6. Pattern Evaluation/Interpretation

Once the trend and patterns have been obtained from various data mining methods and iterations, these patterns need to be represented in discrete forms such as bar graphs, pie charts, histograms etc. to study the impact of data collected and transformed during previous steps. This also helps in evaluating the effectiveness of a particular data model in view of the domain.

7. Knowledge Discovery and Use

This is the final step in the KDD process and requires the ‘knowledge’ extracted from the previous step to be applied to the specific application or domain in a visualised format such as tables, reports etc. This step drives the decision-making process for the said application.

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Advantages of the KDD Process in Data Mining

KDD in data mining is a robust technique to extract valuable insights and knowledge from a large dataset. Several advantages are associated with it, which is why several organizations use it worldwide. Some of these advantages are listed below: 

• Enhances business performance: Employing KDD can assist organizations in improving their operational performance. It helps them identify the areas that require improvement, reduces costs, and optimizes the processes. 
• Helps in decision-making: KDD can help businesses make data-driven and informed decisions by discovering hidden trends, patterns, and relations in the data that might not be easily visible. 
• Increases efficiency: With KDD, organizations can streamline their production processes, optimize the available resources, and enhance their overall efficiency. 
• Saves resources and time: Employing KDD can help businesses save resources and time. This is because the data analysis process is automated, which helps identify the most significant and relevant knowledge or information. 
• Fraud detection: KDD can help businesses identify and detect fraud or fraudulent behavior. It does this by analyzing the patterns in data and identifying any unusual behavior or anomalies.
• Enhances customer experience: By using KDD, organizations can improve the customer experience by understanding customer preferences, behavior, and needs. It also allows businesses to offer personalized services and products. 
• Enables predictive modeling: KDD helps organizations create predictive models to forecast behaviors and trends. This offers the organization an edge over its competitors. 

Disadvantages of the KDD Process in Data Mining

Although employing KDD can have many advantages, it is not entirely free of disadvantages. Some drawbacks of the KDD process in data mining are: 

• Complexity: KDD is a time-consuming and complex process that needs specialized knowledge and skills to perform effectively. Due to this complexity, interpreting and communicating the results with non-experts can take much work. 
• Needs high-quality data: KDD requires high-quality data for generating meaningful and accurate insights. If the data is of poor quality or inconsistent, it may lead to misleading, inaccurate results and inadequate conclusions. 
• High cost: KDD needs specialized hardware, software, and skilled personnel to perform the analysis, which can be expensive. The price might be high for those organizations which have a limited budget. 
• Privacy and compliance concerns: KDD might raise ethical concerns regarding compliance, bias, privacy, and discrimination. For instance, data mining techniques could extract sensitive information regarding individuals without the person’s consent or reinforce stereotypes or biases. 
• Overfitting: The KDD process can often lead to overfitting. It is a common problem in ML where a model learns the details and the noise in the training data to an extent that negatively impacts the model’s performance on new unseen data. 

Difference Between Data Mining and KDD

Data mining and KDD have several differences as listed below:

Parameter	KDD	Data mining
Definition	It is the process of identifying novel, valid, potentially useful, and understandable patterns and relations in data.	It refers to the process of discovering valuable and useful data or patterns from a given large dataset.
Objective	To find helpful knowledge from the given dataset.	To extract helpful information from the given dataset.
Focus	Focuses on the importance of domain expertise in validating and interpreting results.	Focuses on the use of computational algorithms for analyzing data.
Steps	The steps involved include data collecting, cleaning, integration, selection, mining, transformation, pattern evaluation, visualization, and knowledge representation.	The steps involved are data preprocessing, modeling, and analysis.
Outputs	Knowledge bases like models or rules aid organizations in making informed decisions.	A set of relationships, patterns, insights, or predictions to support business understanding or decision-making.
Role of domain expertise	It is necessary for KDD since it helps establish the goals of the process, select appropriate data, and interpret the results.	It is less critical for data mining since the algorithms have been designed to identify patterns without relying on previously known information.

Conclusion

In today’s world, data is being generated from numerous sources of different types and in different formats, for example, economic transactions, biometrics, scientific, pictures and videos etc. With such huge amounts of information being traded each moment, a technique is of utmost importance which can extract the juice and provide reliable, high quality, and effective data for use in various fields for decision making. This is where KDD is so useful.

Data science is a growing field, and almost every business worldwide uses data science tools to expand their business. According to a study, the market size of data science platforms is expected to grow at a CAGR of 27.7% between 2021 and 2026. This indicates the future potential of data science as a career and the growing demand for jobs it will offer. 

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