Technologies used in Data Mining
Simply put, data mining is the process of extracting useable data from a bigger amount of raw data, the process of analyzing big databases in order to generate fresh data.
The interdisciplinary character of data mining research and development adds greatly to data mining’s success and wide-ranging applications.
Here are a few examples of fields that have had a significant impact on the development of data mining methodologies.
Statistics
Statistics is the study of data gathering, analysis, interpretation, and presentation. Data mining is inextricably linked to statistics. Data and statistical models are used in statistics research to produce tools for prediction and forecasting. A collection of data can be summarized or described using statistical methods. Data mining results can also be verified using statistical approaches. For example, after mining a classification or prediction model, statistical hypothesis testing should be used to verify the model.
Machine Learning
Machine learning is the study of how computers learn from data. Computer systems that automatically learn to recognize complicated patterns and make intelligent decisions based on data are one of the key study areas. Machine learning research on classification and clustering tasks frequently focuses on the model’s accuracy. Data mining research emphasizes efficiency and scalability of mining methods on huge data sets, as well as approaches to manage complicated forms of data and investigate new, alternative methods, in addition to accuracy.
Database Systems and Data Warehouses
Large data sets or even real-time, fast streaming data are required for many data mining activities. As a result, scalable database technology can be used effectively in data mining to achieve great efficiency and scalability on big data sets. Furthermore, data mining tasks can be utilized to improve the capabilities of current database systems in order to meet the sophisticated data analysis needs of advanced users.
Information Retrieval
The study of searching for documents or information inside documents is known as information retrieval (IR). Text or multimedia documents can be found on the Internet. Due to the rapid growth of the Web and applications such as digital libraries, digital governments, and health care information systems, vast amounts of text and multimedia data have been amassed and made available online. Their efficient search and analysis have raised numerous data mining difficulties. As a result, text mining and multimedia data mining have become increasingly significant when combined with information retrieval technologies.
Data mining, as a truly interdisciplinary subject, can be characterized in a variety of ways, which is not surprising. Even the word “data mining” does not adequately describe all of the essential elements involved.
