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SYSTEMS FOR KNOWLEDGE DISCOVERY IN DATABASES
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AI
DOI：10.1109/69.250073.png)
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摘要
En 中文
The automated discovery of knowledge in databases is becoming increasingly important as the world's wealth of data continues to grow exponentially, Knowledge-discovery systems face challenging problems from real-world databases which tend to be dynamic, incomplete, redundant, noisy, sparse, and very large. This paper addresses these problems and describes some techniques for handling them. 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.
Keyword:
DATABASES
DISCOVERY
KDD SYSTEMS
MACHINE LEARNING
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期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
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