Data mining was born to solve the difficulty in analyzing the information entered in companies’ databases. Spreadsheets and manual investigations could have added strategic knowledge to the business more efficiently.
With the high volume of data stored in organizations’ databases, there was a need to implement tools to explore and process this information. That’s when data mining gained market attention.
The foundation of data mining comprises three fundamental disciplines. See what the lineages are.
This is the study of links between data. Classic statistics is the basis of data mining technologies, as it is the science responsible for identifying patterns and correlations.
The discipline is focused on data behavior and involves concepts such as averages, normal distribution, variance, covariance, standard deviation, and set analysis, among others.
This science is based on creating technological devices that simulate the human capacity to think and make decisions. Artificial intelligence ( AI) uses statistical data to identify solutions to problems.
Despite trying to mimic human abilities, the resource can still be more efficient than people. The first time an artificial intelligence won a Chinese chess game from a human was in 1997.
And since then, technology has only been accumulating victories. For example, Google’s AI defeated the world champion — Stock fish, which is also a robot — with just 4 hours of training. Made unimaginable for any human being.
Machine learning technology — is a branch of artificial intelligence. The feature makes the machines learn according to the data.
However, this reaction does not only occur with the initial programming information. The machine absorbs new knowledge and learns from interactions with users.
An example of this technology is chatbots. Service robots can add knowledge based on previous contacts . In this way; assistance becomes increasingly natural and accurate.
Netflix uses machine learning to improve the quality of the service stream. After watching a series, the streaming service suggests other titles based on the audience’s interests. In addition, the indications become more and more refined and accurate with the frequent use of the service.
And once again, the machine wins over the human being, as these suggestions can be more efficient in identifying preferences than searches made by the user. After all, who has yet to spend hours in the catalog before choosing something to watch?
Data mining is only possible by combining the three sciences: statistics, artificial intelligence and machine learning. The complementary disciplines ensure consistent data analysis to predict scenarios, identify behavioral patterns and correlations, and make decisions.
What Are The Main Data Mining Techniques?
Before even understanding data mining techniques, defining an objective for analysis is essential. Do you want to know which products are always bought together? Or do you want to understand the profile of customers who purchase these items?
The purposes can be several; however, it is necessary to delimit the desired information and then choose the best technique to obtain results. See some of them.
The concept of neural networks is based on the functioning of the human brain. The nervous system is formed by a complex set of neurons communicating and processing data quickly. The more cells activated simultaneously, the higher the system performance.
In the technological context, neural networks create artificial neurons that mimic the brain’s data processing system. Just as human decisions are based on experiences, the neural network can also store information based on learning and even solving problems.
Remember artificial intelligence mentioned earlier? Neural networks are used to create AI systems. Google, for example, created a tool that can predict the death of patients using neural networks. The technique is also used to forecast sales in commerce. It is possible to anticipate product demand and optimize inventory management through historical data.
This technique consists of a flowchart — in an inverted tree format — that explores all the possibilities of a series of related decisions.
The tree starts with just one node (root) and unfolds into several likely outcomes (branches). Each of these probabilities branches into new hypotheses (leaves).
When the tree has many edges, it can be pruned to facilitate the interpretation of the results. The resource works as a map, and depending on the volume of data, it is easy to follow and understand.
The technique can be used in the health sector to make diagnoses. By placing historical data in the tree, for example, it is possible to build a classification model to diagnose new patients.
This model seeks to identify trends or patterns within a database. In retail, through the basket data, the association rule aims to identify the purchase of items together. For example: when someone buys macaroni, they also buy cheese.
Financial institutions also use this technique for credit assessment, as defaulting customers, for example, may have similar characteristics.
Time Series Analysis
This tactic is directly linked to statistics, a fundamental science in data mining, as already mentioned. Time series analysis uses mathematical models to identify correlations and predict outcomes.
This model can identify behavioral trends, cyclical variations, irregular variations and seasonal fluctuations. A water park, for example, may show seasonal variations with spikes in customers on public and school holidays.
It may seem simple, but the data visualization technique can be useful, especially at the beginning of the data mining process. The information is mapped and transformed into a visual element.
It can be a map of the layout of a store’s products, interactive graphics, and even the form of trees, as already mentioned. With this process, you can visualize the data quality and find the patterns to investigate.