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Data Mining [Complete Guide]

Data mining is the process of sorting through large data sets to identify patterns and relationships that can help solve business problems through data analysis. Different techniques and tools of data mining allow companies to predict future trends and make more informed business decisions.

What is Data Mining?

Data mining is a key part of data analysis as a whole and one of the basic disciplines of data science. It uses advanced analytical techniques to find useful information and understandable patterns in data sets.

Furthermore, data mining is a step in the Knowledge Discovery in Databases (KDD) process, a data science method for collecting, processing, and analyzing data. Data mining and KDD are sometimes referred to interchangeably but are often considered separate subjects.

History and Origins of Data Mining

Data warehousing, BI, and analytics technologies began to emerge in the late 1980s and early 1990s, providing an increased ability to analyze the growing volumes of data that organizations were creating and collecting. they did The term data mining was used until 1995, when the first International Conference on Knowledge Discovery and Data Mining was held in Montreal.

The event is sponsored by the Association for the Advancement of Artificial Intelligence, or AARI, which also holds the conference annually for the next three years. Since 1999, this conference, is commonly known as KDD 2021. It is organized primarily by SIGKDD, the Special Interest Group on Knowledge Discovery and Data Mining in the Society for Computing Machinery.

A technical journal called Data Mining and Knowledge Discovery published its first issue in 1997. Originally quarterly, it is now published bi-monthly and contains peer-reviewed articles on data mining and knowledge discovery theories, techniques, and practices. Another publication, the American Journal of Data Mining and Knowledge Discovery, was launched in 2016.

Why is Data Mining Important?

Data mining is an important part of successful analytics initiatives in organizations. The information it generates can be used in business intelligence (BI) and advanced analytics applications. It includes historical data analysis, as well as real-time analytics applications that continuously examine data over time as it is created or collected.

Effective data mining contributes to various aspects of business strategy planning and operations management. These topics include customer-facing functions such as marketing, advertising, sales, and customer support, as well as manufacturing, supply chain management, finance, and human resources.

Data mining also supports fraud detection, risk management, cybersecurity planning, and many other critical use cases. It also plays an important role in healthcare, government, scientific research, mathematics, sports, etc.

Features of Data Mining

The advantage that we get from data mining are:

  • We can filter out redundant and irrelevant data noise.
  • It helps to make proper use of this information and to correctly evaluate possible outcomes.
  • Increases the speed of informed decision-making.

What are Data Sources?

Data sources can include databases, data warehouses, the web, and other information repositories or data that are dynamically distributed across the system.

Stages of Data Mining

In this part, we want to briefly get to know the general steps in a data mining process. These steps are briefly:

  • Data extraction, transfer, and storage in multidimensional databases.
  • Creating access for business layer data by means of data mining software.
  • Displaying the results of data analysis in the form of graphs or charts.

Types of Data Mining

Each of the following data mining techniques addresses several different business problems and provides different insights into each. However, understanding the type of trading problem you have to solve will also help you know which technique to use is your best choice and will yield the best results.

Types of data mining can be divided into two basic parts as follows:

1. Predictive Analytics

2. Descriptive Analysis

Predictive Data Mining

As the name suggests, predictive data mining analytics works on data that can help predict what might happen in the future in the company. Predictive data mining can be divided into the following five types:

  • Classification analysis
  • Regression analysis
  • Serious analysis of time
  • Predictive analysis
  • Analysis of neural networks

Descriptive Data Mining

The main purpose of descriptive data mining tasks is to summarize or transform assumed data into relevant information. Descriptive data mining tasks can also be divided into four types as follows:

  • Cluster analysis or clustering
  • Summary analysis
  • Analysis of communication rules
  • Analysis and discovery of sequence patterns
  • Data mining with the decision tree method

Data Mining Techniques And Methods

By using data mining techniques, the speed of calculations and the required space in memory (RAM) are significantly improved. In general, data mining techniques can be placed in one of the following 3 categories or a combination of them.


In this type of learning, data is labeled based on defined features and placed into different classes. This algorithm is able to learn the labeling model and use the intelligent learning system to label new samples and separate them. This separation is considered a form of learning and the algorithm can apply its model to new data after this learning.


In this case, the algorithm groups the data based on their nature. For example, it divides the customers of an online store into different clusters based on their similarities (age, gender, level of education, etc.).

Reinforcement Learning

In this learning, the algorithm continuously discovers information and learns by exchanging information and operations with the surrounding environment. For example, consider an algorithm that intelligently designs different types of shopping cart forms by interacting with the environment and simulating it to create the best design for customers and ultimately increase sales and profit

Data Mining Tools

Data scientists use several data mining tools to store, organize, and visualize data. Here are some of the most common ones in use today.


Python is a multi-purpose language that is often used for web development and application development. The language is versatile, easy to learn, and supports many Internet protocols. Because Python is compatible with many libraries and packages used for data analysis, visualization, and machine learning.  It is one of the most important languages ​​for data mining. Python is also open source and free to install, making it a good first language to learn.


SQL or Structured Query Language is essential for data scientists. SQL (sometimes pronounced “sequential”) is a standard language used to communicate with relational databases. Tasks, like adding, deleting, and retrieving data and creating new databases, are done using SQL.

Since data mining requires the ability to work with databases, SQL is an outstanding language. In addition, it is a very common language in business, especially e-commerce where websites store and link large amounts of data about products and customers.


NoSQL (not just SQL) differs from SQL because it works with non-relational databases. Unlike relational databases that store data in tables, non-relational databases can store data based on other methods (such as values ​​or documents). The NoSQL databases can capture structured and unstructured data. As a result, organizations that collect different types of data use NoSQL to manage it.


R is a popular programming language for statistical modeling and graphics generation. Basically, the world of R revolves around data. It includes tools for storing, managing, and analyzing data, as well as tools for displaying the results of that analysis.

In addition, R offers an advanced set of free packages (reusable basic units of code) that can be used for tasks such as visualization, statistical analysis, data manipulation, and more.

Apache Spark

Apache Spark calls itself ” an integrated analytics engine for large-scale data processing,”. An engine that works with many of the platforms listed here. Originally developed at the University of California, Apache Spark runs SQL queries and comes with a machine learning library compatible with other frameworks. It performs streaming analytics. Apache Spark also has a large community that contributes to its open-source code.


Hadoop is a framework for storing large amounts of data on multiple servers, creating a distributed storage network. Data is also copied across different networks as a safety measure. A set of Hadoop modules are used to process and analyze data and can be integrated into many other software platforms (such as Microsoft Excel).

One of the advantages of Hadoop is that it can be scaled to work with any data set, from one on a single computer to those stored on many servers.

Java is a well-known language that runs on multiple devices from laptops to large-scale data centers and mobile phones.

In fact, Java is so widely used that many data mining tools (including Hadoop) are written in Java and installed on top of it. In addition, Java programs can be written on one system and run on any other system running Java.

Why do Businesses Need Data Mining?

With the advent of big data, data mining has become more widespread. Big data or big data are very large collections of data that can be analyzed by computers to reveal certain patterns, sequences, associations, and trends that are perceptible to humans. Big data contains detailed information about different types and contents.

Therefore, with this amount of data, simple manual statistical analysis will not work. This need is met by the data mining process. This work leads to the movement from simple data statistics to complex data mining algorithms.

The data mining process extracts relevant information from raw data such as transactions, photos, videos, and flat files and automatically processes the information to create useful reports for businesses.

Therefore, the process of data mining is crucial for businesses to make better decisions by discovering patterns and trends in data, summarizing them, and understanding relevant insights.

Benefits of Data Mining

The benefits of data mining include the following:

More effective marketing and sales: Data mining helps marketers better understand customer behavior and preferences, enabling them to create targeted marketing and advertising campaigns. Similarly, sales teams can use data mining results to improve lead conversion rates and sell additional products and services to existing customers.

Better customer service thanks to data mining: Companies can identify potential customer service issues faster and provide up-to-date information to call center agents to use in calls and online chats with customers.

Improved supply chain management: Organizations can identify market trends and forecast product demand more accurately, enabling them to better manage inventory and resources. Supply chain managers can also use data mining information to optimize warehousing, distribution, and other logistics operations.

Increase production time by extracting operational data from sensors on production machines and other industrial equipment: supports predictive maintenance programs to identify potential problems before they occur and helps prevent unplanned downtime.

Stronger risk management: Business and risk managers can better assess a company’s financial, legal, cybersecurity, and other risks and plan to manage them.

Lower costs: Data mining helps save money by reducing redundancies and waste in company expenses and operational efficiency in business processes.

Ultimately, data mining initiatives can lead to increased revenue and profits as well as competitive advantages that differentiate companies from their business rivals.

How Does Data Mining Work?

We said about the application of data mining, data mining is a method of solving problems based on available data. At the beginning of this process, your business problems are found. After finding the problems, the information recorded in your organization or production line is received.

Based on the obtained information, the mechanisms related to your business are modeled. Then, using machine learning methods, solutions to eliminate the problems of the organization will be provided to you in the framework of the document report and software.

Based on the mentioned contents, solving the problem with the help of the data mining process takes place in 6 steps, which we will examine in the following:

1- Correct understanding of business

In this case, the employer knows that there is a problem and defect in his work, but he is unable to recognize the problem. Therefore, he raises the problem with the data mining expert, this is the starting point and the first step to solving the problem.

2- Examining and understanding the data

At this stage, the data mining specialist receives data and business information from the employer and reviews them. According to the volume and quality of the data, he modifies the issue raised in the previous stage so that the result of data mining and investigation is presented more realistically.

3- Data preparation

At this stage, the data mining specialist prepares the data, including identifying and removing incomplete and wrong data, integrating different data repositories in the business, etc.

4- Modeling

In the fourth stage, according to different solutions and methods, different models are made and the best model is selected according to the data mining expert.

5- Model testing and evaluation

Now, the formed models are tested and evaluated, and a suitable model is selected that fits the problem raised in the first stage. After this, it is necessary to check the effectiveness of the chosen model during a meeting with the employer.

If the selected model is not suitable and does not help to solve the problems, the process is repeated from the beginning.

6- Development of the final model

If the tests and evaluations are favorable and satisfactory, a number of solutions are provided in the form of developing the final model. The final model specifies how the behavior of the collection should be in the face of the problems raised.

Examples of Data Mining Industry

Data mining techniques are widely accepted among business intelligence and data analytics teams, helping them to extract knowledge for their organization and industry. Examples of data mining applications are:


Online retailers mine customer data and Internet clickstream records to help them target marketing campaigns, promotions, and promotional offers to individual shoppers. Data mining and predictive modeling also power recommendation engines that recommend inventory and supply chain management activities as well as potential purchases to website visitors.

Financial Services

Credit card companies and banks use data mining tools to identify fraudulent transactions, build financial risk models, and apply for loans and credits. Data mining also plays a key role in marketing and identifying potential opportunities to increase sales with existing customers.


Insurers rely on data mining to help price insurance policies and make decisions about approving policy applications, including risk modeling and management for prospects.


Data mining applications for manufacturers include efforts to improve operational time and efficiency in manufacturing plants, supply chain performance, and product safety.


Streaming services perform data mining to understand what users watch or listen to and provide personalized recommendations based on people’s viewing and listening habits.


Data mining helps doctors diagnose medical conditions, treat patients, and analyze X-rays and other medical imaging results. Medical research also relies heavily on data mining, machine learning, and other forms of analytics.

Sales and Marketing

Companies collect vast amounts of data about their customers and prospects. By observing consumer demographics and online user behavior, companies can use the data to optimize marketing campaigns, improve segmentation, cross-sell offers, and customer loyalty programs, and drive more efficiency in marketing efforts. Predictive analytics define the expectations of teams with their stakeholders and provide an estimate of the return on any increase or decrease in marketing investment.


Educational institutions to understand their student population as well as enabling environments; They have started collecting data to improve their performance and process. As courses continue to move to online platforms, they can use different dimensions and metrics to view and evaluate performance; Such as: keystrokes, student profiles, classes, universities, time spent, etc.


Safety is the main driver of data mining in the transportation industry. Cities and communities can conduct traffic studies to determine the busiest roads and intersections, and public transit agencies can mine the data to understand their busiest areas and travel times.

Operational optimization

Process mining uses data mining techniques to reduce costs in operational functions and enables organizations to work more efficiently. This process has helped improve decision-making among business leaders and identify costly bottlenecks.

Fraud detection

While providing valuable insight to teams through recurring patterns in data, observing data anomalies helps companies detect fraud. While this is a known use case in banks and other financial institutions, SaaS-based companies are also starting to adopt these methods to remove fake user accounts from their dataset.

Problems and Challenges of Data Mining

Despite the high importance of data mining in today’s businesses and the important achievements that this science creates for organizations, there are also challenges and problems along the way.

In the following, we will mention some of the most important challenges of data mining and then we will describe some of these cases. The main challenges of data mining are:

  • Security and privacy issues
  • Facing incomplete and scattered data
  • Difficulty discovering complexities in some data
  • Methodological challenges
  • The necessity of choosing the right analysis method to extract efficient results
  • Scalability of algorithms
  • Difficulty in providing intuitive concepts for some phenomena hidden in the data

Future of Data Mining

We live in a world of data; The amount of data we create, copy, use, and store is increasing exponentially. We have already passed the threshold of creating 1.7 megabytes of new information every second for every human on the planet.

This means that the future is bright for data mining and data science. With so much data to sort through, we need more sophisticated methods and models to gain meaningful insights and help make a business decision.

Final Words

Hope you understand the topic completely. If you still have any questions write us in the comment section. we will answer you very soon. Do share with your friends if you like this. Thanks.

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