What is Data Mining?
Patterns, machine learning and visual representation of data. Data mining is a complex process for identifying the correct, new and potentially useful patterns and models in a large amount of data so that these patterns and models are understandable to humans.
How can datamining help online stores?
One of the most prominent examples of data mining can be found in chain stores, which try to identify the relationship between different products when purchasing customers. Chain stores are eager to know what products are sold together.
For example, during a data-mining operation at a North American chain store on a large volume of sales data, it was found that men who go to the store to buy baby bucks usually also buy barley juice. It was also found that customers who buy television often buy crystal vases. A similar example of data mining operations can be seen in a large European apparel production and supply company, in which data mining results indicate that people who buy silk tie-dyes on the same day or days ahead will wear black tie clips. They also buy.
This type of data mining can help stores intelligently organize sales festivals and deliver products to customers.
Another example of sales data mining can be seen in a large North American dubbing and duplication and distribution company where data mining operations, customer relationships and movie actors, as well as different customer groups, are based on the style of the films (scary, romantic, incidental, etc.) was determined.
So the company could intelligently identify potential moviegoers based on customer interest in different actors and cinema styles.
Other uses of data mining include the use of hospitals and pharmaceutical plants to explore patterns and models of the impact of drugs on different diseases and patients of different age groups.
Using data mining in the financial and banking fields leads to identifying high-risk and profitable clients based on criteria such as age, income, residence status, education, occupation and so on.