Data Mining Definition and Techniques
What’s data mining definition?
In its most basic form, data mining is the process of turning data into information and information into knowledge.
Understanding the difference between data, information, and knowledge is essential to accurately define data mining as a whole. Data is any quantity, character, or symbol that can be processed by a computer; information is composed of the patterns, associations or relationships that are derived from data; and knowledge consists of historical patterns and future trends that can be seen/predicted from information.
Data mining processes structured information through the application artificial intelligence, neural networks, and advanced statistical tools in order to detect patterns and summarize data into a format that can be understood.
This allows corporations to uncover the relationships between internal and external business factors that impact customer relations and company success. Put simply, all of this allows corporations to anticipate future trends, uncover new opportunities, and most importantly improve overall performance.
The predictive insights that data mining is capable of providing grants significant advantages to the implementing organization.
Data Mining Techniques
There are three main techniques of data mining that may be of use to an organization, each with their own unique implementation and potential value.
- Association: Association data mining detects recurring themes in databases, identifies relationships between them and develops a pattern of these relationships. It will then use these patterns as a reference to predict future behavior.
Most notably, very complex versions of association data mining is used by Netflix to develop their entertainment recommendations and by Amazon to develop product recommendations during purchases.
- Clustering: Cluster data mining is essentially the stepping stone towards being able to use classification data mining. This technique classifies previously unorganized data into categories that it creates. This can be extremely useful because the software has the capability of detecting very minute similarities or differences that a human analyst would likely not notice and therefore create more accurate/useful categories.
- Classification / Categorization: Classification data mining is used to categorize new data into preexisting categories. It does this by examining the data that has previously been classified, learning the rules of classification and applying those rules to new data.