Knowledge Discovery Process is a user-centered process that identifies the rules and models of tasks involved in extracting knowledge from large databases. This process applies sophisticated data analysis and visualization tools with methods for understanding the users and their tasks. This process is explained in the following paper:

The Process of Knowledge Discovery in Databases:
A Human-Centered Approach

Abstract:

The general idea of discovering knowledge in large amounts of data is both appealing and intuitive. Typically we focus our attention on learning algorithms, which provide the core capability of generalizing from large numbers of specific facts to useful high-level rules; these learning techniques seem to hold the most excitement and perhaps the most substantive scientific content in the knowledge discovery in databases (KDD) enterprise. However, when we engage in real-world discovery tasks, we find that they can be extremely complex, and that low-level data mining is only one small part of the overall process. While others have written overviews of the concept of KDD, and even provided block diagrams for "knowledge discovery systems," no one has begun to identify all of the building blocks in a realistic KDD process. This is what we attempt to do here. In this chapter, we bring into the discussion several parts of the process that have received inadequate attention in the KDD community. Besides providing opportunities for new technologies and tools, a careful elucidation of the steps in a realistic knowledge discovery process can provide a framework for comparison of different systems that are almost impossible to compare without a clean model.

To download a PostScript version of this paper, please click HERE