4. What is the Knowledge Discovery Process?

There is some confusion about the terms data mining, knowledge discovery, and knowledge discovery in databases, we first define them. Note, however, that many researchers and practitioners use DM as a synonym for knowledge discovery; DM is also just one step of the KDP.

Data mining was defined in just add here that DM is also known under many other names, including knowledge extraction, information discovery, information harvesting, data archeology, and data pattern processing.

The knowledge discovery process(KDP), also called knowledge discovery in databases, seeks new knowledge in some application domain. It is defined as the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. The process generalizes to non database sources of data, although it emphasizes databases as a primary source of data. It consists of many steps (one of them is DM), each attempting to complete a particular discovery task and each accomplished by the application of a discovery method. Knowledge discovery concerns the entire knowledge extraction process, including how data are stored and accessed, how to use efficient and scalable algorithms to analyze massive datasets, how to interpret and visualize the results, and how to model and support the interaction between human and machine. It also concerns support for learning and analyzing the application domain.

This defines the term knowledge extraction in a narrow sense. While the acknowledge that extracting knowledge from data can be accomplished through a variety of methods some not even requiring the use of a computer uses the term to refer to knowledge obtained from a database or from textual data via the knowledge discovery process.