What Is Data Mining? | Definition & Techniques

Data mining is the process of extracting meaningful information from vast amounts of data. With data mining methods, organisations can discover hidden patterns, relationships, and trends in data, which they can use to solve business problems, make predictions, and increase their profits or efficiency.

The term “data mining” is actually a misnomer because the goal is not to extract the data itself, but rather meaningful information from the data .

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What is data mining?

Data mining, also known as knowledge discovery in data (KDD), is a branch of data science that brings together computer software, machine learning (i.e., the process of teaching machines how to learn from data without human intervention), and statistics to extract or mine useful information from massive data sets.

Through our online interactions with companies, government agencies, or educational institutes, we produce a large amount of data. This “big data” consists of data sets so large that it’s not possible for a human to analyse them. Instead, this is done with the assistance of a computer.

Data mining transforms this raw data into practical knowledge that helps organisations answer important questions about their users or consumers. Data mining applications include consumer behaviour analysis, sales forecasting, and fraud detection.

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What are different data mining techniques?

Data mining techniques draw from various fields like machine learning (ML) and statistics. Here are a few common data mining techniques:

  • Classification is the task of assigning new data to known or predefined categories. For example, sorting a data set consisting of emails as “spam” or “not spam”.
  • Clustering is the process of grouping data that share common characteristics into subgroups or clusters. Unlike classification (where groups are predefined), clustering is a discovery technique that helps us identify patterns. This allows businesses to create customer segments based on loyalty, communication preferences, or any other trait that emerges from the data.
  • Association rule learning is a technique that looks for relationships between data points. A grocery store chain may use association rule learning to find out which products are frequently bought together and use these insights for promotions.
  • Regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. The goal is to predict the value of the dependent variable based on the values of the independent variables. For example, using historical data about houses with similar characteristics, we might predict the future value of a house.
  • Anomaly or outlier detection is the process of identifying unusual data within a data set (i.e., data that doesn’t follow the general pattern). This data may be interesting (e.g., if it signals a spike in the sales of certain products) or may need further investigation (e.g., if it indicates potential instances of fraud).

How does data mining work?

The data mining process involves using statistical methods and machine learning algorithms to identify patterns in data. Thanks to advancements in computer processing power and speed, analysing data is largely automated.

Although there are different ways to describe the data mining process, a widely used model is the Cross-Industry Standard Process for Data Mining (CRISP-DM), which includes the following stages:

Business understanding

In the business understanding stage, we need to identify the problem we intend to solve through data mining (e.g., how to create a more targeted marketing campaign).

Data scientists and other relevant stakeholders need to define the business problem, which will inform the questions that guide the project. Additional research might be necessary to understand the business context. Determining project goals and success criteria is important for collecting the right data and evaluating the project’s outcomes.

Data mining example: Business understanding
A travel company wants to improve their customer segmentation and develop targeted marketing campaigns for their upcoming trips to various destinations. Their goal is to design effective marketing campaigns that appeal to specific customer segments and ultimately increase bookings.The company sets up a cross-functional team including data scientists, IT-professionals, and marketing managers.

Data understanding

Once the business problem is defined, we need to determine the type of data needed and identify relevant sources. In this step, data scientists collect data from various sources, such as transaction records and customer databases.

However, not every data point may be relevant for the project. For example, a company may only be interested in purchases via credit card. The goal here is to ensure that only the necessary data will be included. By the end of the data understanding stage, the data mining team should have selected the subset of data necessary to address the problem.

Data mining example: Data understanding
The data scientists collect relevant customer data, such as demographics, preferred destinations, travel interests, and feedback. They explore the data to understand its quality, completeness, and suitability for customer segmentation.

Data preparation

Data preparation is the most time-consuming stage and involves several actions to get the data ready for further processing and analysis. This may involve excluding duplicates, missing data, or outliers from the data (i.e., data cleansing).

Data from multiple sources may be merged, organised, or adjusted in different ways to prepare for the next phase. At the end of this stage, the data mining team has identified the most relevant variables and prepared the final data set.

Data mining example: Data preparation
The data scientists clean and prepare the data, addressing missing values, removing duplicates, and ensuring data consistency. Together with the marketing team, they select key variables, such as travel preferences (e.g., destination types, themes, or activities) and customer characteristics (e.g., demographics, interests and hobbies, budget) to create a data set that is ready for analysis.

By studying these variables, the marketing team can eventually create targeted travel offers that cater to their customers’ specific needs and preferences.

Data modelling

Data modelling is the process of organising and understanding data in a structured way. It helps data mining teams find meaningful patterns and insights in the available data.

Data scientists use different models depending on the type of data they have and the problem they’re trying to solve. For example, they might want to identify which products are often purchased together or detect suspicious transactions in banks. To do this, they may use different techniques.

For example, they may apply classification techniques to categorise labelled data or use clustering techniques to group similar data points together. By iterating through this modelling process, data scientists try to reach the best solution.

Data mining example: Data modelling
The data scientists select and apply clustering techniques to identify different customer segments based on travel preferences, past destinations visited, and demographic information.

They build models that group customers into segments that reflect shared travel interests and characteristics. They find out that their customers mainly consist of three distinct groups: “adventure seekers”, “cultural explorers”, and “family vacationers”.

There are two main types of data: labelled and unlabelled.

  • Labelled data means that it has been manually annotated with specific information (e.g., emails labelled “spam” or “not spam”). In this case, data scientists can use a supervised machine learning approach, where the model learns from these labelled examples to make predictions on new, unseen data.
  • On the other hand, if the data is unlabelled, data scientists can use unsupervised machine learning, which helps them discover patterns and relationships within the data without any predefined labels.


During the evaluation stage, the data mining team begins to assess the model’s effectiveness in answering their initial question. This is a human-driven phase, as the project leader needs to decide if the model answers the original question well or uncovers new and previously unknown patterns.

Unlike the technical assessment in the modelling phase, the evaluation phase involves determining which model best meets the objectives and deciding how to proceed. This involves evaluating the results against success criteria, reviewing the process for any oversights, and summarising findings.

The team may decide, for example, to move on to the next phase or, if the model does not align with the desired objectives, to explore alternative models or revisit the data.

Data mining example: Evaluation
The team looks at the progress so far and checks whether the model created can answer the initial question. They assess how well the identified customer segments align with their understanding of the market and check if the segments can guide targeted marketing campaigns for specific travel destinations.


The deployment step is about putting the knowledge and insights gathered from the project into practical use.

Depending on the original question or problem, deployment can be something simple like creating a report or a visual presentation, or something more complex like generating a new sales strategy. Deployment involves integrating the results into the organisation’s operations or decision-making process.

Data mining example: Deployment
Satisfied with the segmentation, the team uses it in their next marketing campaigns. They target each segment with tailored messages, offers, and promotions for specific travel destinations. They monitor the campaigns and measure the impact on bookings. They then use this information to improve their strategy in subsequent campaigns.

Data mining application examples

Here are some real-world examples of data mining:

  • Market basket analysis. Retailers use data mining to analyse large data sets and discover consumers’ buying patterns, such as items that are frequently bought together or seasonal trends. They can use this information to better organise their physical stores or websites, predict sales, and promote deals
  • Academic research. In the field of literary studies, data mining techniques can be used to analyse texts and understand the emotions expressed by authors or characters. Sentiment analysis (or opinion mining) involves using natural language processing and machine learning algorithms to determine the emotional tone of a text.
  • Education. Educational data mining (EDM) aims to improve learning by analysing a variety of educational data, such as students’ interactions with online learning environments or administrative data from schools and universities. This method can help education providers understand what students need and support them better (e.g., through customised lessons or by identifying and engaging with at-risk students before they drop out).

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Frequently asked questions

Is data mining the same as data analysis?

Data mining and data analysis are often used interchangeably. However, they are two distinct processes in the field of data science.

  • Data mining is the process of uncovering hidden patterns, trends, or relationships in large data sets. It involves various techniques like machine learning and statistics, to find useful information in complex data and support decision-making and planning. This process is also called “knowledge discovery.”
  • Data analysis, on the other hand, is a broader term that describes the entire process of inspecting, cleaning, and organizing raw data. The goal is to draw conclusions, make inferences, and support decision-making. Data analysis includes various techniques like descriptive statistics, data mining, hypothesis testing, and regression analysis.

In other words, data mining is one of the techniques used for data analysis when there is a need to uncover hidden patterns and relationships in the data that other methods might miss, while data analysis encompasses a wider range of activities.

Why is data mining important?

Data mining is important because it allows us to discover meaningful patterns and relationships in large volumes of data in a relatively quick and efficient way.

Data mining techniques can take advantage of data coming from different sources like social media platforms or customer databases and convert it into useful insights. In turn, these can answer business or research questions, make predictions, and inform decision making.

What is the difference between data mining and machine learning?

Data mining and machine learning are related fields, but they have different purposes:

  • The goal of machine learning is to develop algorithms that allow computers to learn without human intervention. It’s about making machines smarter, so they can carry out tasks related to human intelligence independently.
  • The goal of data mining is to sift through large data sets and extract useful information like patterns and relationships that can be used to support decision-making. In other words, it’s a tool for humans.

While data mining and machine learning have distinct goals, there is some overlap in their applications. Machine learning can be used as a means to conduct data mining by automatically detecting patterns in data. On the other hand, data gathered from data mining can be used to teach machines and improve their learning capabilities.

In short, data mining and machine learning can complement each other, but they are distinct in their purposes and applications.

Sources for this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

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Nikolopoulou, K. (2023, July 20). What Is Data Mining? | Definition & Techniques. Scribbr. Retrieved 18 June 2024, from https://www.scribbr.co.uk/using-ai-tools/what-is-data-mining/


Yağcı, M. (2022). Educational data mining: prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9(1). https://doi.org/10.1186/s40561-022-00192-z

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Kassiani Nikolopoulou

Kassiani has an academic background in Communication, Bioeconomy and Circular Economy. As a former journalist she enjoys turning complex scientific information into easily accessible articles to help students. She specialises in writing about research methods and research bias.

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