Data mining technology – intelligent data processing using machine learning methods, mathematical statistics and database theory.

The term “data mining” appeared in the 1990s, but as such data processing arose in the 18th century, based on the Bayes theorem, a little later on regression analysis.

As the amount of data grew, new technologies in the field of computer science were invented (neural networks, genetic algorithms, decision tree method, etc.), it became possible to store large amounts of data and increase the speed of information processing by computers, interest in data mining grew rapidly and data processing soon became a separate discipline. Now data mining includes processing not only text data (text data mining), but also graphic and multimedia (web mining).

Currently, data mining is part of a larger concept – Big data, which in addition to data processing includes its collection and storage.

You can apply the technology wherever data is available. The most widely used Data Mining technology in retail, banking, telecommunications, and insurance.


Data Mining and Retail: Similarity, Time, Predictive Model Analysis

Organizations that engage in retail trade collect data on each completed purchase and solve several problems:

  1. Product similarity analysis

Data mining systems help you find the purchases that the buyer usually makes together. For example, for shoes, the vast majority of consumers can also buy shoe cream. This allows you to immediately offer additional goods and offer more, improve advertising, correctly layout goods in retail outlets, etc.

  1. Time analysis

If a customer bought a flashlight today, it will be useful for the company to find out how long it will take to buy batteries. This allows you to properly create inventory.

  1. Predictive models

Retailers can recognize the needs of different categories of customers and their behavior in order to create more accurate advertising campaigns.


Data Mining and Banking: Fraud Prevention and Customer Segmentation

With the help of Data Mining, banks solve such issues:

  1. Fraud Prevention

The bank can analyze past cases of fraud, find patterns and are more likely to avoid similar cases in the future.

  1. Customer segmentation

A bank can make its marketing strategy more focused and effective if it breaks its customers into subcategories.


Application of Data Mining in Telecommunications: Analysis of Call Records and Search for Loyal Clients

Thanks to technology, companies are able to:

  1. Analyze call records

This is useful because it helps to identify categories of customers with similar patterns of using their services and develop the most attractive offers for them.

  1. Find the most loyal customers

To allocate funds to where the most tangible returns.


Data Mining and Insurance Companies: Risk Analysis and Fraud Prevention

Features of Data Mining:

  1. Risk Analysis

Companies can identify how to reduce their losses. For example, an insurance company may find that the amounts paid to people who are married are higher than the amounts claimed by single people. In this case, the company may change its marketing strategy and begin to provide discounts to married people.

  1. Fraud Prevention

The insurance company can analyze past cases of fraud, find patterns in claims for insurance claims, and are more likely to avoid similar cases in the future.


Fundamentally, data mining is based on 3 concepts:

  • Mathematical statistics – is the basis of most technologies used for data mining, for example, cluster analysis, regression analysis, discriminatory analysis, etc .;
  • Artificial intelligence – reproduction of the neural network of human thinking in digital form;
  • Machine learning is a combination of statistics and artificial intelligence that helps computers understand the data that they process to select the most appropriate analysis method or methods.

The following main classes of tasks are used in data mining:

  • deviation detection – identification of data that differ in some parameters from the total mass;
  • association training – finding relationships between events;
  • clustering – a grouping of data sets without previously known patterns;
  • classification – a generalization of a known template for application to new data;
  • regression – search for a function that displays a data set with the smallest deviation;
  • summarizing – display in a compressed form the source information, including the provision of reports and visualization.