The Knowledge Discovery Associates process consists of five steps. Each step raises issues and questions about the outcome of the earlier steps. Thus, at each step, earlier stages are reviewed to achieve deeper understanding, which is measured on an extra dimension, called the "what" to "how" dimension.
The steps in the knowledge discovery process are:
Starting with issues, concerns, and general objectives, a problem description evolves.
Finally, a problem specification is arrived at. This includes quantifiable measures for
later test and validation.
Background and contextual knowledge, prior practice, rules of operation, etc., are first recorded and then encoded as computable objects.
Phases include encoding the data dictionary and data field semantics, sample selection, and data cleaning. Technical issues addressed include missing data fields, data uncertainty, and ordering of events in time.
Selection, application, integration, and customization of data mining and data analysis methods.
Test, validate, evaluate, implement, and report. Results must be provably novel, useful, and understandable.