Title :
Systems for knowledge discovery in databases
Author :
Matheus, Christopher J. ; Chan, Philip K. ; Piatetsky-Shapiro, Gregory
Author_Institution :
GTE Labs. Inc., Waltham, MA, USA
fDate :
12/1/1993 12:00:00 AM
Abstract :
Knowledge-discovery systems face challenging problems from real-world databases, which tend to be dynamic, incomplete, redundant, noisy, sparse, and very large. These problems are addressed and some techniques for handling them are described. A model of an idealized knowledge-discovery system is presented as a reference for studying and designing new systems. This model is used in the comparison of three systems: CoverStory, EXPLORA, and the Knowledge Discovery Workbench. The deficiencies of existing systems relative to the model reveal several open problems for future research
Keywords :
deductive databases; knowledge acquisition; knowledge based systems; learning (artificial intelligence); CoverStory; EXPLORA; KDD systems; Knowledge Discovery Workbench; future research; idealized knowledge-discovery system; knowledge acquisition; knowledge discovery; machine learning; real-world databases; Data analysis; Deductive databases; Filters; Information retrieval; Laboratories; Machine learning; Packaging; Pattern analysis; Relational databases; Transaction databases;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
