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Data mining

What is data mining and what are its uses?

In recent years, some marketing research in American stores has shown that customers who come to the store to buy milk usually also buy bread. In this way, the sales of these goods increased and contributed a lot to the prosperity of those stores. The primary data in this research is the type of people’s purchases that makes this data usable is data mining. In this article, we want to introduce you to this magical science.

What is data mining?

Today, companies gain a lot of information by providing services and continuous communication with the customer. It will be very profitable if they know how to use this data. Data mining is simply a problem solving method that extracts repetitive patterns by analyzing large volumes of data. It then provides solutions to challenges by finding connections between different events and these patterns. In fact, data mining discovers valuable results from information that may not be useful and makes them usable.

Data mining is a powerful science that can penetrate everything and answer many of our unknowing questions. Today, the importance of this science in large companies is so good for that. Before deciding and planning to conduct specialized campaigns or design expensive products. They first seek to obtain public data.

What is the importance and application of data mining?

In a world where most communication is free of space and time and everything is based on virtual communication. Getting information from customers overlook will be a great blessing for companies. Although organizations strive to maintain customer relationships and sales, many of the world’s technology flagships are still not easily accessible. Perhaps one of the great secrets of this success is the benefit of data mining knowledge. In some of these companies, data mining is so important and entrenched that they even launch data collection campaigns.

Recently, a campaign called 10Years Challenge was launched on social networks such as Instagram, Twitter and Facebook, during which people posted pictures of the situation now and 10 years ago. The challenge was well received by users around the world and caused controversy in the media. Because some sources, who have not yet been confirmed or rejected, consider this challenge as Mark Zuckerberg’s new trick to test Facebook’s face recognition algorithm. If this is true, Zuckerberg has probably been able to gather a large amount of diverse and new data in the best possible way.

In fact, organizations that use data mining to analyze competitors and markets will be able to predict current trends. Therefore, in the future plans of the company, it goes in the direction of public demand and attracts customers’ attention before other competitors.

This is true in other fields such as science and health, politics and even economics. The data are very useful in issues such as examining the patterns of virus outbreaks and the effectiveness of drugs, observing public feedback on the actions of politicians, and even in stock market decisions.

Today, data mining in cases such as:

Public health: which is to spread the culture of health at the lowest cost, in different parts of the world.

Customer Purchasing Market Research: This topic, which is a kind of application of data mining in management, seeks to identify goods related to the customer’s shopping cart to increase their purchasing power.

Education: The activity of this field is to improve the quality of the educational system.

Construction: The effort of this area is to facilitate road construction and optimal urban patterns due to population growth.

Customer Relationship Management (CRM): The goal is to improve customer relationships with companies and increase productivity.

Prevent e-banking attacks: Used to identify attack algorithms.

Criminal and criminological investigations: Data mining is for investigating the connections between criminal incidents.

And it has applications in many other areas.

An Example

Consider a social network like Instagram. The user publishes an image on their page, thus creating a new data in this application. Now other people who have followed this person see that image which produces a new data every time this image is. Like and comment by different users ‌ also creates new data. Imagine that this simple process is daily in many applications and generates several terabytes of data.

The further we go in terms of time, the more data is generated and accelerated, and as they increase, the question arises as to how this data can be processed. Can this larger data set be processed by storage systems and methods that previously worked for smaller datasets? The short answer is: no.

As data production speeds increase and their volume becomes much larger, traditional methods, such as conventional algorithms, are no longer able to process this volume of data in a reasonable amount of time. For example, imagine the same social network Instagram that has a lot of users and photos and likes and comments.

Suppose on this social network we want to identify two people with similar interests out of several million users and introduce them to each other as a suggestion to follow each other. Using a normal algorithm will probably take many years to do. Because the temporal complexity of this issue is “exponential”. But the good news is that new methods have emerged to develop such systems, known as data mining and machine learning techniques.


According to statistics, about 2 exabytes (ExaByte), or about 1 million terabytes (TraByte) of data is transferring daily. This huge amount of data creates the concept of big data (BigData) that encourages companies to use data mining knowledge. Therefore, one of the most essential success factors for different businesses in the near future is to make decisions using data. .