Data mining methods extract insights from collected data using various tools. They are used in fraud protection, marketing, and surveillance. Modern techniques use automated concepts to deliver substantial data via computerized resources. The commonly used method is Knowledge Discovery in Databases (KDD). Basic data mining methods involve four types of tasks: classification, clustering, regression, and association. SIGKDD determines which processes are appropriate and publishes them in SIGKDD Explorations.
There are several data mining methods used in both software options and theoretical concepts. These allow users to extract insights from data collected from individuals and companies using a variety of tools. Large amounts of data can be used to determine various factors in a single subject or in a variety of subjects. These data mining methods are most commonly used in the fields of fraud protection, marketing, and surveillance.
For hundreds of years, data mining methods have been used to extract information from subjects. Modern techniques, however, use automated concepts to deliver substantial data via computerized resources. With the emergence of computer sciences during the 20th century, the concept of data mining methods developed in an attempt to overcome hidden patterns in large areas of collected data. A good example of this is when an advertising firm analyzes a customer’s buying patterns online. This company can then market certain products that the individual may be interested in purchasing.
A commonly used data mining technique in the industry is called Knowledge Discovery in Databases (KDD). Developed in 1989 by Gregory Piatetsky-Shapiro, KDD allows users to process raw data, analyze information for the data they need, and interpret the results. This method allows users to find patterns in algorithms, however the overall data is not always accurate and can be pieced together in compromising ways. This is known as overfitting.
Basic data mining methods involve four particular types of tasks: classification, clustering, regression, and association. Classification takes the information present and combines it into defined groupings. Clustering removes defined groupings and allows data to be classified by similar items. Regression focuses on information function, modeling data on concept. The final data mining method, association, attempts to find relationships between various data feeds.
When using the various data mining methods, certain standards are used to determine which parameters can be used in the process. The Association for Computing Machinery’s Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) holds an annual meeting to determine which processes are appropriate. Ethical factors are weighed alongside practical applications to find the best information about individuals and companies. This information is published in an industry magazine called SIGKDD Explorations.
Protect your devices with Threat Protection by NordVPN
