What is Data Mining?
Data mining is a process to extract information from a data set and transform it into an understandable structure for further use. It refers to the process that attempts to discover patterns in large volumes of data. It uses various methods like artificial intelligence, machine learning, statistics, and database systems.
Data mining is a sub-field of computer science and statistics with an overall goal to extract information.
After the raw data analysis stage, it involves various concepts like database management, data processing, data modelling, and inference considerations, interest metrics, Theory of computational complexity, post-processing of discovered structures, data visualization, and online update.
Data Mining is all about: collection, extraction, storage, analysis, and statistics including artificial intelligence, machine learning, and business intelligence.
Data Mining Objectives
The task of data mining is the automatic or semi-automatic analysis of large amounts of data. This serves to extract exciting patterns hitherto unknown. We talk about the groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rules mining).
This generally involves the use of database techniques such as spatial indexes. Thus, these patterns can be seen as a kind of summary of the input data. In addition to being able to be used in additional analysis or, for example, in machine learning and predictive analysis.
One of the examples we can give is data mining. This could identify several groups in the data, which can then be used to obtain more accurate results — being able to predict problems through a decision support system.
Neither data collection, data preparation, nor interpretation of results and information is part of the data mining stage. However, they belong to the entire KDD process as additional steps.
The Applications of Data Mining Models
Data mining patterns and trends are also known as Data Mining models. There are four essential applications where Data mining models are used mostly.
- Forecasts. Calculation of sales or prediction of loads on servers, or even estimate the time that the server remains idle.
- Risks, and probabilities. It is a scenario where we seek to distinguish the best customers to send mail. Thus, being able to determine a balance in terms of risk and the probability of results.
- Search sequences. The analysis of the items that the customers added themselves to their shopping cart. Through the extracted data or obtained data mining models, possible future events can be predicted.
- Grouping. This scenario seeks to divide customers or events into groups of elements that are related to each other. In this way, you can perform an analysis and predict affinities.